scoring body condition in the ferret: development of a

Scoring body condition in the ferret: development of a method based on comparative analysis of condition scoring methods in the cat.

Student: Inge Bertijn

Studentnummer: 4258460

Begeleider: Yvonne van Zeeland

Datum: 17-5-17

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Table of Contents Abstract ................................................................................................................................................... 3

Keywords ................................................................................................................................................. 3

Review methodology ............................................................................................................................... 4

Abbreviations .......................................................................................................................................... 5

Introduction ............................................................................................................................................. 6

Part 1:Current available techniques for scoring the body condition in cats ........................................... 7

The gold standard: DEXA ..................................................................................................................... 7

Techniques that evaluate body condition ......................................................................................... 14

Body Condition Score .................................................................................................................... 14

Muscle mass score......................................................................................................................... 17

Morphometric measurements ...................................................................................................... 20

Techniques that estimate body composition .................................................................................... 25

Bioelectrical impedance analysis ................................................................................................... 25

Ultrasonography ............................................................................................................................ 30

Discussion .......................................................................................................................................... 32

Conclusion ......................................................................................................................................... 33

Part 2: Individual project. Evaluating the body condition of the ferret ................................................ 35

Material and Methods ....................................................................................................................... 35

Results ............................................................................................................................................... 42

Discussion .......................................................................................................................................... 53

Conclusion ......................................................................................................................................... 54

Acknowledgements: .............................................................................................................................. 55

References ............................................................................................................................................. 56

Appendices ............................................................................................................................................ 65

Appendix 1 The 9-point BCS system .................................................................................................. 66



Appendix 4 S.H.A.P.E. flow chart and table ....................................................................................... 69

Appendix 5: Muscle mass score chart cat ......................................................................................... 71

Appendix 6: Phase 1 registration table ............................................................................................. 72

Appendix 7: Phase 1 registration table, morphometric measurements ........................................... 73

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Abstract To evaluate the body condition of animals, various methodologies exist, including morphometric

methods as BCS, fBMI and MMS and the chemical-analytical methods BIA and DEXA. However, up to

this day no method has been developed or validated for the evaluation of the body condition of the

ferret. In order to develop a BCS-system for the ferret, first a literature review was performed to

compare existing methods in cats, following which a BCS method was developed for the ferret for

use in practice. To develop the BCS-chart, 41 ferrets were visually inspected and evaluated on the

palpability of different bone processes. In addition, morphometric measurements as body length,

belly circumference, ribcage circumference and a leg index measurements were also taken. Using

these measurements, a BCS-chart was developed that enables ferrets to be classified as obese,

underweight or in optimal condition. Although further research will be needed to validate this BCS-

chart, it is expected to serve as a valuable tool for assessing body condition of ferrets for both pet

owners and veterinarians.

Keywords Ferret, body condition, BCS, evaluation of body condition.

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Review methodology For this thesis, the databases Pupmed, Cab abstracts and Scopus have been searched. The keywords

used for the search are listed in table 1. Combinations of the keywords were made within categories

and between categories, usually combining a keyword for a specific category with an animal species.

The keywords for the individual project were combined with the keyword ‘ferret’. Google was used

while searching the keywords ‘ggplot2’ and ‘glmulti’ for data analyses purposes. Articles were

selected based upon title and abstract descriptions. Articles validating and/or using different

techniques to evaluate body condition were eventually used in the thesis. If possible, reviews were

avoided. Only when no regular articles could be found, reviews were used. Some articles were found

by reading the references of selected articles or reviews.

Table 1: searched keywords per category

subject researched Keywords

Cat Cat, felis

Ferret Ferret

Rabbit Rabbit

Horse Horse, equine

Cow Cow, cattle

Other species laboratory animals, pets

General information Diagnosing obesity, body condition, photograph, body condition score system, scoring system, body condition tool, obesity, body fat, body mass index, measurement of body composition

Introduction Model, diseases, common diseases, obesity, ferrets as laboratory animals

DEXA Dual energy x ray absorptiometry, DEXA, DXA, beam hardening, validation, cross calibration, precision, accuracy, radiation dose, validation phantom

Body condition score BCS, body condition score system

MMS Estimating lean body mass, muscle mass score, muscle mass, muscle condition score, muscle wasting, prognosis

Morphometric measurements

Morphometric methods, morphometric techniques, zoometric methods, zoometric index, zoometry, body measurements, body fat index

BIA Bio impedance monitoring, bioelectrical impedance analysis, multifrequency bioelectrical impedance analysis, bioelectrical impedance, bioimpedance phase angle, MFI BIA

ultrasound ultrasonography, ultrasound, ultrasonic fat meters

Individual project Anatomy, body weight, weight, body condition (combined with ferret keywords)

Individual project: data analysis

Glmmulti, ggplot2

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Abbreviations

Abbreviation Full meaning

95% CI 95% confidence interval

BC Belly circumference

BCS Body condition score

BCS-chart Body condition score chart

BMC bone mineral content

BF% body fat percentages

BIA Bioelectrical impedance analysis

BW Body weight

CV Correlation of variance

DBL Dorsal body length

DEXA Dual energy X-ray absorptiometry

ECW Extracellular water

fBMI Feline body mass index

FM fat mass

ICW Intracellular water

LBM lean body mass

LIM Leg index measurement

MF-BIA Multi frequency – BIA

MMS Muscle mass score

OR Odds ratio

PA Phase angle

R Resistance

RC Ribcage circumference

Re Extracellular water resistance

R∞ Total body water resistance

SF-BIA Single frequency-BIA

SFL Subcutaneous fat layer

S.H.A.P.E. Size, Health and Physical Evaluation

TBW Total body water

VBL Ventral body length

Xc Reactance

Z Impedance

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Introduction The ferret (Mustela putorius furo) is a domesticated carnivorous animal that is most likely an

descendant of the European Polecat (Mustela putorius putorius). It is believed that ferrets have been

domesticated for around 2000 years now, even though they were already mentioned by

Aristophanes and Aristotle 450 and 350 BC (1).

Over the last century, ferrets have become increasingly popular as pets across the world. Estimations

of the number of pet ferrets kept in the US range from 1 million to 7-10 million ferrets (2–4).

Although no exact information is available for the general European situation, in the Netherlands

20.000 to 30.000 ferrets are kept as pets and hunting animals (5). These numbers are likely to be

similar in the rest of Europe.

Working ferrets have been used for centuries to hunt wild rabbits in a practice known as ferreting

(1). Nets are placed over the rabbit holes while the ferrets hunt them out. The ferreter is then able to

humanely dispose the ferret (6).

Aside from their popularity for hunting and as companion animals, ferrets are also used as laboratory

animals for biomedical research (3,7,8). As laboratory animals, ferrets are being used as a model for

human viral pathogens (influenza viruses), cardiovascular research, nutrition research and

gastrointestinal disease among other researches (3,8–10). It has been estimated that around 1.1

million ferrets are being used as laboratory animals in the US (3). European numbers, however are

not known.

An objective assessment of the body condition of the ferret can be very useful for veterinarians to

keep track of ferret health. Just as any other animal, ferrets can develop a great array of diseases.

Ferrets especially are prone to the development of tumours, cardiovascular, renal, and endocrine

disorders (2,11). Gastrointestinal disease (e.g. Helicobacter associated gastritis) is also common (12).

For many of these diseases, weight loss is the most prominent indicator of disease. In laboratory

ferrets with experimental infections, this is no different (7). Laboratory ferrets are therefore weighed

to assess their change in body condition in studies on, for example, viral disease (13). Although very

rare, ferrets can also develop obesity related illnesses (14).

Being able to properly estimate the body condition is thus very important for both veterinarians,

researchers and pet owners. The body condition can be scored and evaluated by different methods.

For various animal species, e.g. dogs, cats, horses, cows and rabbits, multiple standardized methods

have been developed and validated (15–19). These methods include BCS, morphometric

measurements, DEXA scans and others. To the author’s knowledge, a system to objectively evaluate

the body condition of ferrets has not yet been developed or validated. Furthermore, it is known that

pet owners often misperceive the body condition of their pets when they are in suboptimal condition

(20,21). This will most likely also be the case for pet ferret owners. Therefore, in this pilot study, a

first attempt to develop a method to objectively evaluate the body condition of the ferret in a clinical

setting will be made.

Although nothing has been made for ferrets, lots of techniques to grade the body condition have

been developed for cats, the domestic pet that resemble ferrets the best (17,22). (17,22). Hence, a

method to objectively evaluate the body condition of the ferret in a clinical setting will be made,

based upon the already existing techniques for the cat.

The first part of this thesis will provide an overview of the currently available techniques for

determining body condition in cats. In the second part, the individual project, in which a method to

evaluate the body condition is developed, will be described.

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Part 1:Current available techniques for scoring the body condition in

cats The body condition of an animal is determined by the amount of body fat and muscle mass the

animal possesses. Even though no real reference values are known, the cutoff values between lean

and optimal weight are considered 80% lean body mass and 20% body fat (23–25). Obesity is often

defined as having more than 25-35% body fat.

Techniques that evaluate an animals body condition can be subdivided in two different categories:

techniques that evaluate body condition and techniques that estimate body composition. The golden

standard, DEXA, can also be included in this last category, but will be described before the

techniques that evaluate body condition, because it is used as a reference method. An overview of all

described methods can be seen in Table 6.

The techniques will be compared to each other based on their general principles, reliability and

practical availability and applicability. A reliable technique is both precise and accurate. The precision

of a technique is subdivided into repeatability (intra-observer variability) and reproducibility (inter-

observer variability) (26,27). When an method is precise, an observer will assign the same score to

the same animal on separate occasions and another observer will agree with that score. Accuracy is

defined as the ability of the method to predict the actual body condition of the animal, as measured

by the gold standard (26).

The gold standard: DEXA Although DEXA estimates the body composition and can thus also be discussed in the third chapter

of this introduction, it is considered the gold standard for the evaluation of the body condition in

alive humans1 (29). The method is used as a reference method for validating other techniques in

animals, suggesting that DEXA has become a gold standard for body composition measurements in

alive animals as well (25,30–32). Therefore, this technique is discussed first. DEXA was originally

developed to measure bone density and bone mineral content, but it’s potential to estimate body

composition of humans and animals was quickly discovered (33). Since then, the technique has been

widely used in humans, but also in laboratory- and companion animals for both purposes (34). The

system measures fat mass (FM), body fat percentages (BF%), lean body mass (LBM) and bone mineral

content (BMC), making it a three compartment method

1 DEXA is not considered to be the true gold standard for body composition measurements in animals. Chemical analysis is (28). However, to execute this technique the animal has to be euthanized, making the applicability of this technique rather limited. For this reason DEXA is discussed here as the gold standard method.

8

General principle

The technique uses an X-ray source (placed under the patient), that produces x-rays with two

different photon energies. These X-rays are attenuated when passing through the body tissues,

which is then measured by a detector placed above the patient (Figure 1). Each type of tissue (fat

mass, lean mass and bone) attenuates the low and high frequency photon energies in varying

degrees. Because the attenuation of bone, fat- and lean tissues are known, an estimate can be made

from the total attenuation of soft body tissues to determine the amount of LBM and FM (35,36). In

pixels containing only soft body tissues, the percentages LBM and FM are directly calculated. In the

pixels containing bone and soft tissue, DEXA can only differentiate between the soft tissue and bone

mineral content (37).

Over the years, three generations of DEXA scans have been developed, i.e. the pencil-beam, fan-

beam and - more recently - the narrow fan-beam densitometers (38,39). Pencil-beam densitometers

scan the body in a rectilinear pattern (see Figure 2). However, this results in a relatively long scanning

time of approximately 20 minutes (39). To improve scanning speed and resolution, the fan-bean

densitometer was developed (see Figure 2). However, the shape of the fan beam causes a significant

magnification of structures closer to the x-ray source compared to more peripherally located

structures (38). As a result, the modern narrow fan beam densitometer was developed, which uses a

combination of the two previously developed tactics to both minimize scanning time and reduce the

magnification effect (see Figure 2) (39). Most of the DEXA scans in cats are performed with fan beam

Figure 1: Principle of action of a DEXA scan. The same principle is used for both bone health assessments

as body composition assessments (30).

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scanners (40–44). Only Zangi et al. (2013). and Speakman et al. (2001) used pencil beam scanners

(33,36).

Precision

DEXA scans need to be calibrated daily with calibration phantoms to ensure that values measured

are repeatable on the same densitometer (45). These phantoms consist of materials that mimic the

physiological range of body compositions, tissue thickness or bone density (46). Materials like acrylic

and polyethylene are used to imitate fat mass. Adding bars of aluminium or calcium hydroxyapatite

will make the phantom mimic bone also (Figure 3) (47). Specific capsuled spine phantoms for

measuring BMD are on the marked. The phantoms can also be used for cross calibration, for which

they will be scanned 10-30 times per scanner (45,47,48).

Figure 2: The different type of DEXA scanners and their scanning patterns (37).

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Reproducibility (variability between scanners)

The reproducibility of DEXA scans is variable if cross calibration is not applied. As described above,

lots of devices and versions of densitometers have been made. When one wants to compare results

from different DEXA devices, cross calibration is often necessary, because differences of 0.8% - 8.4%

for LBM and FM estimates are seen (47,49–51). A 2% difference between measurements of FM, LBM

and BF% and a 1% difference for BMD is deemed acceptable (47). However, even when an device is

replaced with an device of the same brand and type, differences in FM, LBM and BF% measurements,

before calibration can exceed 2% (47). These differences between results are resolved by

recalibrating devices with phantoms or by using cross-calibration equations (45,47,52). Cross

calibration equations are developed by scanning human subjects on both scanners. With these

results an equation is developed to convert results from one scanner to be comparable with results

from the other scanner (45). After calibration with phantoms, difference between scanners can be

reduced to <0.05% (47).

Repeatability (variability within one scanner)

Three short-term repeatability studies in cats have been performed in which 4-10 consecutive scans

were made (Table 2; (40,42,53). Borges et al. (2008) also investigated the effects of repositioning

between scans (42). Of the various measurements made, the FM was found to be least repeatable in

cats. In total 13 cats were scanned wit fan beam scanners, whereby Munday et al. (1994) and Borges

et al. (2008) found a coefficient of variance (CV) for FM measurements of 5.58% and 7.7%,

Figure 3 An example of a total body phantom. This is the modern BioClinica Body Composition Phantom

(BBCP), developed by Bioclinica Inc, Princeton, NJ (47)

11

respectively (Table 2) (40,42). In contrast, Lauten et al. (2000) scanned only one cat with a pencil

beam scanner for 6 consecutive times, finding a CV for FM of only 1.77% (53). When the cat is

repositioned between scans with a fan beam scanner, the precision of BF% measurements decreases

even further to a CV of 10.9% (42). LBM measurements in cats have a lower CV, as can be seen in

Table 2 These results are comparable with humane literature (48,54).

The CV ‘s of Lauten et al. (1994) were all <1% (except for FM), indicating that the pencil beam

densitometer may be more precise. Repositioning of the cat resulted in significantly higher values of

CV with a fan beam densitometer, underlining the importance of using the exact same body

placement as much as possible. Most cats are placed in dorsal/sternal recumbency on the DEXA

densitometer (30,36,42,44,53). Cats are either placed with hind limbs and forelimbs extended caudal

(42,53), with hind limbs extended caudal and forelimbs extended cranial (44) or with hind limbs and

forelimbs extended cranial (30). A ventral recumbency is sporadically also used (25). It is not known

which position produces the most consistent results.

Research Number of cats CV FM CV LBM Scanner type

no repositioning between scans: CATS

Lauten et al. (2000) (53) 1 cat (6 consecutive scans)

1.77 0.34 Pencil beam (Lunar DPX-L)

Munday et al. (1994) (40) 5 cats (4-10 consecutive scans)

5.58 0.92 Fan beam (Hologic QDR 1000/W)

Borges et al. (2008) (42) 7 cats* (5 consecutive scans)

7.7 3.2 Fan beam (Hologic QDR 4500 Elite)

no repositioning between scans: HUMANS

Bilsborough et al. (2014) (48)

25 humans † (2 consecutive scans)

5.9 0.5 Pencil beam (Lunar DPX-IQ)

22 humans † (2 consecutive scans)

2.5 0.3 Fan beam (Lunar Prodigy)

repositioning between scans: CATS

Borges et al. (2008) (42) 7 cats* (5 consecutive scans)

10.9 4.3 Fan beam (Hologic QDR 4500 Elite)

repositioning between scans: HUMANS

Barlow et al. (2015) (54) 45 humans (2 consecutive scans)

1.6 2.3 Narrow fan beam (GE Lunar iDXA)

*The same 7 cats were used. First 5 scans without repositioning were made, after which another 5

scans with repositioning between each scan were made.

† Derived from a pool of 36 humans

Table 2: CV of DEXA BF% and LBM measurements in cats, as measured by different studies

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Accuracy

Even though DEXA is used as a gold standard to validate methods that evaluate body condition, it has

been validated by comparing it to other highly respected techniques as chemical analysis and

deuterium dilution. Deuterium dilution is a method in which deuterium, a stable isotope of hydrogen

is injected intravascular, after which its concentration is measured in a physiological fluid to estimate

TBW (36).

In cats, only the accuracy of pencil beam DEXA scans has been evaluated. Upon comparison of the

pencil beam DEXA scanner with chemical analysis of body tissues of cats (and dogs), DEXA correlated

well for measurements of LBM, water content, FM and bone mineral concentration (BMC), with

correlations between results of the DEXA scan and chemical analysis ranging between 0.909-0.996

and mean errors ranging from 1.6% to 2.6% (33). However, individual errors can be quite large,

particularly for the analysis of body fat percentages (ranging from 20.75% to 31.5%), resulting in a

low accuracy of the DEXA scan on an individual level (33). Size of the error in fat content as analysed

by DEXA appear to be related predominantly to water content of the muscle, with larger errors

occurring upon a lager decrease or increase of the water contents of the muscles. This measurement

error is largely based on the fact that estimations of LBM are based on the assumption that LBM has

a fixed water content of 73%. If the water content of tissue increases or decreases, body fat

measurements using DEXA will subsequently overestimate or underestimate the true BF%,

respectively (33). Adequate tissue hydration is therefore considered essential for obtaining reliable

DEXA results in cats. However, in the humane literature it has been shown that the fat error caused

by tissue hydration changes is small when the range of tissue hydration compatible with life is

considered (see box 1) (55). Changes in hydration of 1-5% are measured to lead to an error in BF% of

<1%. Therefore tissue hydration alone cannot explain the high individual errors for BF% estimates in

cats. However, no other explanation was found.

Compared with deuterium dilution, the pencil beam DEXA scanner underestimated LBM in cats by

9.2%. In accordance with Speakman et al. (2001) (33), this research also found a high mean error for

the estimation of FM (23.3%). However, DEXA results correlated well with the deuterium-dilution

estimated LBM (r2=0.841) and FM (r2=0.867), demonstrating the high accuracy of DEXA scans (36).

In humans, where the use of DEXA has also been validated using deuterium analysis and chemical

analysis of pork carcasses, two sources of error have been identified that have to be considered

when using the technique (56–58). They are further explained in box 1 combined with the relevance

for cats.

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Practical applicability & availability

Animals need to lie completely still in order to obtain a successful DEXA scan. As a result, sedation or

anaesthesia will usually be required. The necessity to sedate or anesthetize an animal simultaneously

limits the use of DEXA for routine measurements of body condition, especially if the animal is

considered to have an increased anaesthetic risk (e.g. sick or geriatric animals) (36). Furthermore, the

requirement of a constant, adequate tissue hydration for obtaining reliable results inhibits the

applicability of DEXA measurements in animals with extreme fluid accumulations (e.g. in case of

congestive heart failure) or severe dehydration.

Moreover, DEXA scanners require a lot of space. Also, most primary veterinary clinics do not have the

financial funds to acquire and operate a DEXA scan, rendering the technique less suitable for in the

first line veterinary practice. However, fan beam DEXA scanners are often successfully used in weight

loss studies and for validation of other body condition evaluation techniques in cats (25,44,59,60).

Another perceived disadvantage of routine use of DEXA scan is the potential exposure to radiation.

However, radiation doses emitted during DEXA scans are relatively low, and generally far lower than

the background radiation in the Netherlands. As a result, DEXA scans should be considered safe

(52,61–63).

Sources of error for DEXA

Tissue hydration

Adequate, constant tissue hydration is considered important for acquiring reliable DEXA results

and preventing errors, because the estimations of LBM are based on the assumption that LBM

has a fixed water content of 73%. However in the humane literature doubts are expressed if

tissue hydration even has a significant effect on DEXA results, with errors <1% in BF

measurements caused by 1-5% changes in the hydration status of subjects (50). Carlson and

Costello calculated in their book that the error of hydration status in LBM and FM estimates

would not exceed 0.5 kg, considering that only 8% of the extra water would be mistaken for FM

(54).

Beam hardening

Beam hardening is caused by the preference of tissues to attenuation low energy photons. When

the X-ray beam is sent through thicker tissues, more high energy photons will pass the tissue

than low energy photons. Because most low energy photons are already attenuated in the first

centimetres of tissue, the beam hardens and less attenuation is seen deeper in the tissue. Thus

attenuation per cm will be lower than in a thinner tissue. Since this attenuation is used to

estimate the FM, the accuracy of FM estimates by DEXA will be dependent on the tissue

thickness of an animal (55). When the tissue thickness is under 5 cm, an underestimation of the

FM is seen. With a tissue thickness above 20 cm, DEXA is likely to overestimate the FM. Some

software packages include corrections for the effects of beam hardening (55), but the effects can

also be reduced by filtrating the photons with the lowest energy from the beam before sending it

through the patient (56). However, in cats, beam hardening errors are less likely to influence the

results since errors are low for tissue thicknesses between 5- 20 cm

Box 1: Sources of error for DEXA measurements

14

Techniques that evaluate body condition The techniques that evaluate body condition evaluate the outer appearance of the animal.

Estimations of fat, muscle mass and body size are made. The body condition score (BCS), muscle

mass score (MMS) and morphometric measurements will be described.

BCS systems and morphometric measurements evaluate an animals body condition based on the

animals body shape and fat covering (43,59,64–66). The technique tries to estimate BF%. Muscle

wasting can therefore easily go undetected without a thorough examination of muscle mass when

the animal is not underweight. Loss of muscle mass is an important sign of disease that should not be

missed during a veterinary examination (64). Therefore a MMS for cats has been developed as

described below.

Body Condition Score The first body condition score (BCS) system for cats was developed in the 90’s (59). It comprises a

subjective, non-invasive system that is based upon visual inspection and palpation to determine the

body condition. A 5-, 6-, 7-, or 9-point scale have been developed, often accompanied with

descriptions and lateral and dorsal drawings or photographs of the animal to aid the user in scoring

(43,59,64–66). Scoring is done based upon the shape of the body, visibility and palpability of skeletal

structures (e.g. ribs and vertebrae) and palpable fat in the abdomen and over the ribs, which

indirectly assesses the amount of abdominal- and subcutaneous fat present in the animal.

General principle

The 9-point BCS system was first developed and validated by Laflamme et al. (1997) (59). This system

(Purine BCS system) currently is one of the most widely accepted and used BCS systems and can be

divided into three main categories: 1) animals which are underweight, represented by scores 1 to 4;

2) animals with an ideal weight, represented by score 5; 3) animals that are obese, represented by

scores 6-9 (59). Every score is accompanied with a description, whereas images are only provided for

scores 1, 3, 5, 7 and 9 (see Appendix 1).

The 5 point system, validated by Shoveller et al. (2014), is very similar to the 9-point system (for the

score chart see Appendix 2), whereby Points 1 and 2 represent the animals that are underweight;

point 3 the animals in ideal body condition and point 4 and 5 the animals that are obese (67). Similar

to the 9-point system, visual aids and descriptions of important areas of interest to correctly

determine the body condition are given (66). Some veterinarians prefer to grade half points, which

basically turns this system into the 9-point system as described above (68).

The 6-point system uses a chart with six cat shapes combined with key-words to describe the body

condition of the cat: 1 (cachectic), 2 (lean), 3 (optimal lean), 4 (optimal), 5 (heavy) and 6 (obese)

(43,69). No further description of the body conditions are given (Appendix 3).

The 7-point system is one of the more recently developed systems, designed by WALTHAM. The

system, which is called S.H.A.P.E (i.e. Size Health And Physical Evaluation), has been developed to

both increase usability for non-experienced observers and enhance the reproducibility (70). It is

based on an algorithm and uses most of the same visual and manual inspections as the other BCS

systems. However, no images or drawings of body shape are given. Instead, this algorithm provides

the observers with a set of questions in a flow chart, guiding them through the observations and

examinations that have to be made (see Appendix 4). The system uses the letters A (underweight) to

G (obese) to describe the different categories.

15

Precision

Four studies have tested the reproducibility of 5-, 7- and 9- point BCS systems (59,65,67,71). Scores

given by trained observers were compared with each other and with scores given by untrained

observers (Table 3). For untrained observers owners and other untrained staff were employed.

Trained observers were defined as veterinarians, veterinary technicians and other staff trained in

evaluating body conditions. Only two studies investigated the repeatability of the BCS systems. In the

research of Laflamme et al. (1997), six experienced observers each scored the same cats twice with

the 9-point purina BCS system (59). The observations were done several days apart, while six

observers were blinded for their previously given scores and for the scores given by the other

observers. Hawthorne et al. (2005), on the other hand, measured scores of eight unexperienced

observers for the same BCS system on one occasion (71).

Reproducibility (inter-observer variability)

BCS systems generally show high levels of agreement between skilled observers regardless of the

scale used. During various studies that were performed, correlation between trained operators

ranged from 0.89 to 0.987 (Table 3;(59,70). Veterinarians and other skilled or trained observers

usually have a higher agreement in their assessments than owners or untrained observers and

veterinarians (67,70,71). Scores given by untrained observers in a 5-point system without pictures

differed significantly from the scores given by trained observers (65). However, when the 9-point

system without pictures was used, the difference between trained and untrained observers is not

significant. This might be explained by the fact that the 9 point system has a larger scale and allows

owners to better nuance their cats body condition even without pictures. The correlation between

experienced and unexperienced observers increases vastly in both the 5- and 9-point system when

pictures are used (65). Nevertheless, the 7-point system (S.H.A.P.E.), which does not use pictures,

also shows high correlations between scores of untrained- and expert observers (70). Most likely

because the untrained observer is guided step by step through the process of evaluating the body

condition. The 6-point system has, to the authors knowledge, not been tested on reproducibility,

making it impossible to know what the correlations are.

The aforementioned data suggest that the 7-point system is the most reproducible method (Table 3).

However, the 5-, 7- and 9-point systems can all be used reliably to score the body condition. If

images are not used, the 9-point system is preferred above the 5-point system.

9-point BCS system 5-point BCS system S.H.AP.E. (7-point sytem) (70)

Expert – expert Expert –amateur Expert – expert Expert –amateur Expert – expert Expert –amateur

r2 = 0.89 (59) No pictures: r2 = 0.554 (65)

Kappa = 0.752 (67)

No pictures: r2 = 0.499 (65)

r2 = 0.987 r2 = 0.864 & r2 = 0.867

CV = 15.3% (71)

With pictures: r2 = 0.721 (65)

With pictures: r2 = 0.736 (65)

Kappa = 0.499 (67)

Table 3: Correlations between the scores of expert (trained) observers and amateur (untrained) observers

when using the different BCS systems

16

Repeatability (intra-observer variability)

Laflamme et al. (1007) found a high correlation of 0.95 between the scores of the observers,

indicating that repeatability with the 9-point BCS system is high (59). Hawthorne et al. (2005), on the

other hand, measured a moderate repeatability with a CV of 15% (71). However, Laflamme et al.

(1997) used experienced observers, while Hawthorne et al. (2005) employed inexperienced

observers, explaining the lower correlation. For the other BCS systems, repeatability is not known in

cats nor in dogs, but one can assume that the repeatability would also range between moderate to

good, considering they are based on the same principles.

Accuracy

Body condition scoring systems are validated by comparing them with percentages body fat as

measured by Dual Energy X-ray Absorptiometry Analysis (DEXA). Besides Laflamme et al. (1997),

three other validation researches have been conducted by Hawthorne et al. (2005). Shoveller et al.

(2014) and Bjornvad et al. (2011) (25,59,67,71). 32, 60, 133 and 72 cats were used in the studies,

respectively. Shoveller et al. (2014) applied a 5-point scale, while the rest used a 9-point system. All

studies compared the cats assigned body condition scores with DEXA results. Borges et al. (2012)

compared DEXA results with scores of a 9-point BCS system among other methods in 16 cats

undergoing a weight loss program on three stages in their weight loss (41).

BCS systems are highly correlated with body fat percentages, as measured with DEXA (5-point:

r2=0.8, 7-point: r2= 0.83 and 9-point: r2=0.73-0.92; (59,67,70,71). The highest correlations with BF%

are seen when the scores of a trained observer are used (59,67). Each step in the 9-point system, or

half step in the 5-point system, was found to correlate with a 5-7% increase in body fat percentage

(59,67). However, it should be noted that the mean BF% per BCS category differs between active and

relatively inactive cats. Inactive cats have a higher body fat percentage in each category of the BCS

than active cats, reflecting their smaller amount of muscle mass (25). BCS systems barely pay

attention to muscle mass, and therefore are unable to successfully identify the cut-off point between

an ideal and unideal body condition in the right categories (23). This could also explain the high CVs

(13.9%-25.8%) between body fat and BCS categories found by Borges et al. (2012) (41).

The phenomenon is described as Skinny Fat’, a term also used in human literature (67,72). Skinny fat

cats have a higher BF% than desirable even though they have an ideal BCS. Skinny fat people have

higher health risks, compared to fatter, but fitter people (72). This should be kept in mind when

grading the body condition of a cat with a BCS system.

Practical applicability and availability

BCS systems are generally easy to use and non-invasive, requiring no expensive equipment and

enabling scoring to take place outside of the veterinary practice without sedation or anaesthesia. The

systems are nowadays widely used inside and outside veterinary practices by owners an

veterinarians, in order to reliably keep track of the body condition of cats and dogs. Especially in

otherwise healthy animals, the BCS is a great way to specify the body condition.

However, the measurements are subjective and training is required to make the observations more

reliable. Owners tend to normalize their animals body condition while using the BCS (20,21). The

difference in opinion between veterinarian and owner can interfere with owner compliance when

suggesting a weight loss program. This should not be overlooked.

17

Muscle mass score The muscle mass score (MMS) is a relatively new system for evaluating the body condition of cats

and dogs (64,68). In contrast to the BCS and many other body condition scoring methods, this scoring

system does not focus on body fat to obtain an impression of the animals general body condition but

rather assesses the muscle condition. As such, it can be used to complement the BCS system.

General principle

For the evaluation of the MMS in cats, the muscles mass over the scapula, temporal bones, ilium

wings and spine is visually inspected and palpated (see WSAVA score chart, Appendix 5). The amount

of muscle wasting is then graded on a 4-point scale ranging from 0 to 3, whereby severe muscle

wasting is classified as 0, moderate muscle wasting as 1, mild muscle wasting as 2, and normal

muscle mass as 3 (Figure 4; 67).

Precision

To date, only two studies have evaluated the precision and accuracy of this new MMS system

(73,74). Linder et al. (2013) compared MMS with BCS in 87 dogs. Michel et al. (2011) made 10

veterinarians and veterinary technicians score the MMS of 44 cats on three different occasions, after

which these results were compared with DEXA scans. Despite the lack of data on the precision and

validation of the MMS a short discussion of what is known will follow.


In the study of Michel et al. (2011), the inter-observer variability of the MMS was found to be high.

Inter-rater agreement for the MMS system between 10 observers participating in this study was

moderate with correlations between the observers for the categories ‘normal’ and ‘severely wasted’

ranging between 0.48 and 0.59 (73). However, little agreement was seen for the intermediate MMS

categories 1 and 2, with inter-rater agreement ranging between 0.20 and 0.31. This suggests that

fusing the intermediate MMS categories to a 3-point model could increase the reproducibility of this

new method. However, further research will be necessary to determine whether this adjustment

would result in an acceptable reliability.


In contrast to the reproducibility, Michel et al. (2011) found the repeatability of the MMS system to

be higher. Correlations between the observers’ scores in three separate evaluations were found to

be acceptable (i.e. 0.71 - 0.73; (73). However, the repeatability has been studied in only 10 observers

and with at least a weak apart, making the first and last observations a minimal of 2 weeks apart.

Since muscle wasting can occur rapidly in diseased animals (73), the muscle mass of some animals

could potentially have changed over this period.

18

Figure MMS

MMS = 3 Normal muscle mass.

MMS = 2 Mild muscle wasting

MMS = 1 Moderate muscle wasting

MMS = 0 Severe muscle wasting

Figure 4: A graphic demonstration of the categories from the MMS. AHAA

Nutritional Assessment guidelines (75)

19

Accuracy

When compared with DEXA, the MMS in cats is significantly and positively correlated with LBM.

However, correlation between the two parameters is low (r2=0.62). This can be explained due to the

fact that LBM does not only exist out of muscle. For LBM%, MMS had a significant, but low, negative

correlation with the LBM% as measured by DEXA (73). This can be explained by the fact that the

LBM% increases in a leaner animal, or in an animal losing weight, even though the total LBM in grams

decreases (75). This decrease in LBM might be picked up with the MMS system, resulting in the weak

negative correlation as seen. The MMS only has a weak correlation with BCS (r2=0.47-0.76; (73,74).

Considering the fact that the BCS mainly focuses on body fat, this is understandable.

Practical applicability & availability

The figures described above show that the MMS system is in its early stages of development and lots

can still be improved. The system as it is used right now is not very accurate or precise, making only

very broad assessments possible. Just as the BCS, the MMS does not require expensive equipment, is

non-invasive and only minimal patient compliance is necessary to perform the evaluation. Therefore

the technique can also be used outside the veterinary practice.

In the future the MMS system can be of added value to the BCS in sick, older and obese animals. By

assessing muscle mass separately, muscle wasting as a consequence of diets can be earlier detected

and addressed, making weight loss regimes safer (64). Also, a more objective assessment of muscle

mass in sick animals will allow a veterinarian to keep track of the changes in their body condition.

Besides this, the MMS can help the veterinarian to objectively differentiate between potential animal

cruelty cases and severe animal disease by being able to distinguish between stress starvation and

simple starvation. Stress starvation is caused by severe clinical disease. Simple starvation, in contrary,

is caused by a lack of food intake which could be the result of neglect (64).

The amount and presence of muscle wasting, as described in human literature, has an direct

influence on the prognosis of disease (76,77), making the MMS a promising potential prognostic

value for the veterinary world, while supplementing the BCS.

20

Morphometric measurements Morphometric measurements have been used for at least 25 years to estimate a cat’s body condition

(28). Besides in cats, the technique has also been applied in dogs and rabbits (15,78). Measurements

used among others include height, length, girth, thoracic circumference, pelvic circumference, paw

circumferences, head circumference, limb length, leg index measurements. In contrast to humans,

measuring skin fold thickness is not considered a reliable method for estimating BF%, because most

animals have a rather loose skin and subcutaneous tissue, rendering it difficult to accurately measure

the amount of subcutaneous fat present (28).

General principle

Multiple methods have been developed to apply these morphometric measurements. In principle,

two gross applications of morphometric measurements can be distinguished. The measurements can

be used alone, or to complement other body condition scoring methods. Predictive equations have

been developed containing morphometric measurements and other techniques, for example

bioelectrical impedance analysis (BIA) and sonography (28,41). By combining the methods in an

equation, accuracy is enhanced. Using these equations, BF%, LBM, FM, total body water (TBW) and

body weight (BW) (28,31,71,79–81) of cats can be estimated (Table 4). Alternatively, predictive

equations have been developed containing only morphometric measurements

Methods only applying morphometric measurements include the feline body mass index (fBMI) and

other unnamed systems developed by Stanton et al. (1992) and Witzel et al. (2014; (28,31). Mixed

equations have been developed by Stanton et al. (1992) and Borges et al. (2012; (28,41).

fBMI

At least three different fBMI systems have been developed over the years (71,80,82,83). All use

different equations and definitions of BMI.

The oldest BMI for cats has been described by Nelson et al. (1990; (83), and Hoenig et al. (2013; (82).

It uses a BMI equation based on a measurement of the amount of body weight per body surface area

(Kg/m2; Table 4). For this purpose, body length, body height and body weight are measured, whereby

body length is defined as the distance between the scapula point and the tuber ischium, and body

height is defined as the distance between the point of the scapula through the elbow and the

proximal boundary of the central metacarpal pad.

A different fBMI system estimates the BF% of cats. It was originally developed by Hawthorne et al.

(2000) and patented in 2005 (71,81). This fBMI method uses two morphometric measurements, i.e.

the ribcage circumference, which is highly correlated with BF%, and the leg index measurement (LIM)

that shows little correlation with BF%. Thoracic circumference is measured at the 9th rib with a tape

measure. The LIM is measured as the distance between the patella and the calcaneal tuber a

standing cat. It is used to measure the stature of the animal to correct the thoracic circumference to

the animal’s size, making it possible to estimate BF% based upon the thoracic circumference of an

animal. The predictive equation can be seen in Table 4, where a BF% of 25% is considered an ideal

body condition, as described by Hawthorne et al. (2005) based on data collected at the Waltham

Centre for Pet Nutrition (WPCN) (71).

Kawasumi et al. (2016) developed the most recent fBMI system, to improve accuracy and lower the

complexity of the method. It is a combination of the methods as described above, using body weight

and PCL (i.e. the distance between the patella and the top of the calcaneus in the standing cat). It is

21

comparable to the LIM in the fBMI of Hawthorne et al. (2000 & 2005). The fBMI is in this system

expressed in kg/m (Table 4), with values ≥ 28 are considered overweight.

Estimate Morphometric equations: fBMI Correlation with DEXA

Body height and weight-derived fBMI (82,83)

𝑓𝐵𝑀𝐼 (𝑘𝑔 𝑚2⁄ ) = 𝑏𝑜𝑑𝑦 𝑤𝑒𝑖𝑔ℎ𝑡 (𝑘𝑔)

𝑏𝑜𝑑𝑦 𝑙𝑒𝑛𝑔𝑡ℎ (𝑚) 𝑋 ℎ𝑒𝑖𝑔ℎ𝑡 (𝑚)

unknown

Thoracic circumference-derived fBMI (71,81)

𝐵𝐹% = [(𝑡ℎ𝑜𝑟𝑎𝑐𝑖𝑐 𝑐𝑖𝑟𝑐𝑢𝑚𝑓𝑒𝑟𝑒𝑛𝑐𝑒

0.7067 − 𝐿𝐼𝑀)

0.9156] − 𝐿𝐼𝑀

0.85

PCL-derived fBMI (80)

𝑓𝐵𝑀𝐼 (𝑘𝑔 𝑚⁄ ) = 𝑏𝑜𝑑𝑦 𝑤𝑒𝑖𝑔ℎ𝑡 (𝑘𝑔)

𝑃𝐶𝐿 (𝑚)

Unknown

Other morphometric equations

TBW (28) 𝑇𝐵𝑊(𝑘𝑔) = (0.65 ∗ 𝑏𝑜𝑑𝑦 𝑤𝑒𝑖𝑔ℎ𝑡) − (0.03 ∗𝑝𝑒𝑙𝑣𝑖𝑐 𝑐𝑖𝑟𝑐𝑢𝑚𝑓𝑒𝑟𝑒𝑛𝑐𝑒) + (0.04 ∗ 𝑟𝑖𝑔ℎ𝑡 ℎ𝑖𝑛𝑑 𝑙𝑖𝑚𝑏 𝑙𝑒𝑛𝑔𝑡ℎ) − 0.031

0.98*

LBM (31) 𝐿𝐵𝑀 = 30.3(ℎ𝑒𝑎𝑑 𝑑𝑖𝑎𝑚𝑒𝑡𝑒𝑟 ∗ ℎ𝑖𝑛𝑑 𝑙𝑖𝑚𝑏 𝑙𝑒𝑛𝑔𝑡ℎ +316.9 (𝑓𝑜𝑟𝑒𝑙𝑖𝑚𝑏 𝑐𝑖𝑟𝑐𝑢𝑚𝑓𝑒𝑟𝑒𝑛𝑐𝑒) + 2.55 ∗ 0.85(𝑡ℎ𝑜𝑟𝑎𝑐𝑖𝑐 𝑑𝑖𝑎𝑚𝑒𝑡𝑒𝑟 ∗ 𝑓𝑜𝑟𝑒𝑙𝑖𝑚𝑏 𝑙𝑒𝑛𝑔𝑡ℎ) +14.4(𝑏𝑜𝑑𝑦 𝑙𝑒𝑛𝑔𝑡ℎ) − 3.0587

0.85

FM (31) 𝐹𝑀 = 436.9(𝑏𝑜𝑑𝑦 𝑤𝑒𝑖𝑔ℎ𝑡) −24.0(ℎ𝑒𝑎𝑑 𝑑𝑖𝑎𝑚𝑒𝑡𝑒𝑟 ∗ 𝑓𝑜𝑟𝑒𝑙𝑖𝑚𝑏 𝑙𝑒𝑛𝑔𝑡ℎ) −309.2(𝑓𝑜𝑟𝑒𝑙𝑖𝑚𝑏 𝑐𝑖𝑟𝑐𝑢𝑚𝑓𝑒𝑟𝑒𝑛𝑐𝑒) + 2.5227

0.98

BW (84) BW = -4.53 + 0.11(Wither height) + 0.13 (Body Length) (79) 0.57

Mixed equations

FM (41) 𝐹𝑀 = 0.4(𝐵𝑜𝑑𝑦 𝑤𝑒𝑖𝑔ℎ𝑡) + 0.006𝑅( 𝐵𝐼𝐴) + 9.67𝑆𝐹𝐿 − 0.69 0.94

FM (41) 𝐹𝑀 = −0.005(𝑏𝑜𝑑𝑦 𝑙𝑒𝑛𝑔𝑡ℎ) + 0.7(𝑏𝑜𝑑𝑦 𝑤𝑒𝑖𝑔ℎ𝑡) +0.007𝑅(𝐵𝐼𝐴) − 0.60 0.98

FM (28) 𝐹𝑀 = 0.04(𝑝𝑒𝑙𝑣𝑖𝑐 𝑐𝑖𝑟𝑐𝑢𝑚𝑓𝑒𝑟𝑒𝑛𝑐𝑒) − 0.004(𝑙𝑒𝑛𝑔𝑡ℎ2 𝑅(𝐵𝐼𝐴)⁄ −0.08(𝑟𝑖𝑔ℎ𝑡 𝑓𝑜𝑟𝑒𝑙𝑖𝑏𝑚 𝑙𝑒𝑛𝑔𝑡ℎ) + 1.11 0.93*

LBM (28) 𝐿𝐵𝑀 = 0.74(𝑏𝑜𝑑𝑦 𝑤𝑒𝑖𝑔ℎ𝑡) + 0.11(𝑟𝑖𝑔ℎ𝑡 𝑓𝑜𝑟𝑒𝑙𝑖𝑏𝑚 𝑙𝑒𝑛𝑔𝑡ℎ) +0.02(𝑏𝑜𝑑𝑦 𝑙𝑒𝑛𝑔𝑡ℎ) − 0.03(𝑝𝑒𝑙𝑣𝑖𝑐 𝑐𝑖𝑟𝑐𝑢𝑚𝑓𝑒𝑟𝑒𝑛𝑐𝑒) −0.001𝑅(𝐵𝐼𝐴) − 1.50

0.98*

BF% (28) 𝐵𝐹% = −0.02(𝑙𝑒𝑛𝑔𝑡ℎ2 𝑅(𝐵𝐼𝐴)) − 4012(𝑟𝑖𝑔ℎ𝑡 𝑓𝑜𝑟𝑒𝑙𝑖𝑏𝑚 𝑙𝑒𝑛𝑔𝑡ℎ)⁄ +1.48(𝑝𝑒𝑙𝑣𝑖𝑐 𝑐𝑖𝑟𝑐𝑢𝑚𝑓𝑒𝑟𝑒𝑛𝑐𝑒) −1.16(𝑐𝑟𝑎𝑛𝑖𝑎𝑙 𝑡ℎ𝑜𝑟𝑎𝑐𝑖𝑐 𝑐𝑖𝑟𝑐𝑢𝑚𝑓𝑒𝑟𝑒𝑛𝑐𝑒) + 92.93

0.82

* Equations are compared with chemical analysis as reference method, instead of DEXA

Table 4: Different equations containing morphometric measurements and their correlations with DEXA.

References of the equations are displayed in the ‘estimate’ column.

22

Other morphometrical methods:

Apart from the BMI, other morphometric equations have been developed (able 4). For example,

Stanton et al. (1992) developed an equation to estimate TBW using body weight, pelvic

circumference and right hind limb length (28). Furthermore, Witzel et al. (2014) developed two

equations that estimate LBM and FM in overweight or obese cats, by comparing morphometric

measurements with DEXA results (31). Equations have also been made to estimate body weight in

cats, using a variety of morphometric methods (84,85).

Examples of mixed equations combining morphometric measurements with DEXA-, sonography- or

BIA values, estimating FM, FM%, LBM and body weight, can also be seen in Table 4 (28,41). The

mixed equations are developed with stepwise-regression analysis, while choosing the dependent

variables from reference methods as DEXA and chemical analysis.

Precision

Witzel et al. (2014a & b) have, in two separate studies of dogs and cats, tested the reproducibility

and repeatability of single morphometric measurements in dogs and cats (31,78). Four investigators

took each measurement twice. Repeatability was tested by Hawthorne et al. (2005) by 8

investigators who took each measurement in duplicate (71).


Reproducibility of independent morphometric measurements and full equations is generally high,

dependent on the measurements used. Inter-observer variation ranges between <2% and 5% for

most independent measurements, for example body length, thoracic circumference and limb length

(31). In dogs inter-observer variation was actually found to account for <1% of the total variation of

the developed equations estimating LBM, FM and BF% (78). However, some measurements in cats

show greater variations than 10%, with metacarpal and metatarsal pad width & length and forelimb

circumferences having variations between 16.4%-19.5% (31).

The reproducibility of the full fBMI equation by Hawthorne et al. (2005) is, just as independent

measurements, high with a CV of around 10% (81). For the other equations and systems, no

reproducibility data is known.


Morphometric measurements will generally have a high repeatability. Intra-observer variations are

lower than 10% for most individual measurements (71,81), but variances as low as <2% have been

reported (31), comparable with reproducibility results. Independent measurements are thus

comparable within and between investigators.

Accuracy

Most studies compare the morphometric measurements with DEXA results, making predictive

equations using multiple regression analysis (28,31,41,71). Chemical analysis, however, can also be

used as a reference method (28).

Thoracic circumference and girth measurements have been found to correlate well with DEXA BF%,

with correlations of 0.83 and 0.77, respectively (81). When considering the full equations, in which

multiple techniques or measurements are included, correlations with DEXA tend to increase to 0.85

and 0.98 as can be seen in Table 4 (31,41). By adding more explanatory variables, the accuracy

increases, because more of the observed variation in the animals can be explained by the model.

23

All fBMI systems correlate with DEXA- and BCS-determined BF%. However, the PCL-derived fBMI of

cats with BCS 5/5 overlap the fBMI values found in previous BCS categories, with values ranging from

29.9 to 40.3 (80). For BCS 5, the correlation with PCL-derived fBMI values is thus low. Values of the

body height- and weight derived fBMI system for obese cats were significantly higher than values in

lean cats in the body height- and weight-determined fBMI system (86). The same significant

difference was seen for DEXA BF% measurements, suggesting that the fBMI values are correlated

with BF%. However, to the author’s knowledge, no further validation studies have been performed,

limiting the ability to draw definitive conclusions about the validity of this system.

By adding the LIM to the thoracic circumference measurements, the fBMI of Hawthorne et al. (2005)

achieved a correlation of 0.85 with DEXA BF% (81). This is higher than the correlation between DEXA

BF% and the 9-point BCS system, rendering this fBMI method more reliable than BCS systems and

other fBMI methods.

The equations made by Witzel et al. (2014) for the estimation of LBM and FM only obtain

morphometric measurements, but nevertheless these were found to be highly correlated with DEXA

results (31). The LBM equation correlates well with DEXA LBM with an correlation of 0.85, whereas

the estimated FM had an even higher correlation of 0.98. Another equation only using morphometric

measurements, estimating TBW, showed a correlation of 0.98 with TBW as estimated by chemical

analysis (28). These high correlations combined with low standard errors show that morphometric

measurement equations can be highly accurate in predicting body condition parameters . However,

even though Witzel et al. (2014) included 76 cats in their study, all of them were overweight or

obese. The equations estimating LBM and FM are only valid in overweight or obese cats and cannot

be extrapolated to the entire cat population.

Mixed equations show high correlations for estimated FM, LBM and BF% (0.82-0.98) with low

standard errors (28,41). However, it is doubtful if these equations are representative for the entire

cat population. For example, the number of cats used in the studies by Borges et al. (2012) and

Stanton et al. (1992) was low (n=16 and n=22; (28,41). Moreover, the equations of Borges et al.

(2012) have solely been based on measurements in obese cats that underwent a weight loss

programme, rendering the accuracy of these equations in lean or underweight cats questionable and

necessitating. further research to confirm the findings and validate the techniques.


Predictions of BF% can be used to determine the ideal body weight and energy requirements of an

animal (81). This can aid the veterinarian in establishing a diet plan and target weight for overweight

animals. The morphometric measurements can thus be used to treat pet obesity without the need

for expensive weight control programmes that include DEXA scans.

Similar to BCS and MMS, the use of morphometric measurements to estimate body condition

parameters is a cheap method that can be applied virtually anywhere. For most equations, only a

tape-measure and a scale are required. Moreover, little training is necessary to apply the techniques,

making it possible for measurements to be performed by untrained owners or technicians (71).

Because of the non-invasiveness of the techniques, no sedation is necessary, making it possible for

old and sick animals to be evaluated without extra risks. However, patient compliance can become a

problem when many measurements need to be taken or when faced with an hyperactive animal,

which can particularly be challenging in cats.

24

However, the equations consist of 2 up to 6 different measurements (31,41,87), taking on average 5

minutes to perform (31). In practice, where clinic consultations usually only last 10 minutes, these

measurements will thus take up half of the consult, rendering it impossible to include this in the

standard examination and necessitating extra time to be scheduled and charged for enabling these

measurements to be performed. As it is unlikely that an owner is willing to pay extra for the time

needed to accurately determine the body condition of their cat, this could limit the application of

these techniques in a practical setting. However, solutions to this problem are certainly conceivable,

e.g. by training veterinary technicians to take the measurements.

Lastly, as described above, study populations used to develop these equations need to be kept in

mind. For example, the equations that estimate LBM and FM (Table 4), can only be used for

overweight cats, considering the equations was made based solely on overweight animals. These

type of study errors can greatly limit the applicability of an equation.

Thus, when developed properly, equations based on morphometric measurements can give highly

accurate and precise estimations of parameters as LBM, FM, BF% and fBMI with a better reliability

than the BCS. Combined with the absence of highly invasive and expensive techniques, these

equations are very interesting for application in veterinary practice.

25

Techniques that estimate body composition The techniques that estimate body composition make an attempt to measure the exact amount of

fat and/or lean body mass. The composition of the body is analysed. In this category bioelectrical

impedance analysis (BIA) and ultrasonography will be considered.

Bioelectrical impedance analysis

Bioelectrical impedance analysis (BIA) was originally developed to estimate bone density. However,

nowadays it is also used to estimate the LBM and the FM of humans and multiple different animal

species (23,28,88–90).

General principle

BIA is a technique that estimates total body water (TBW) and extracellular water (ECW) by measuring

the resistance (R) and reactance (Xc) of a small electrical, alternating current that is send through the

body between 2 or more electrodes, usually needles (32,91). The electrodes can be placed in

different configurations on the cats body, as described by Elliot et al. (2002) and Stanton et al. (1992;

(87,92). With the TBW, estimates of FM and LBM can subsequently be made. Estimates can also be

made with the use of predictive equations formulated from multiple regression analysis (41,87). The

fundamentals and basic principles of BIA will be explained briefly below. For more information the

author refers the reader to the reviews of Khalil et al. (2014), Jaffrin et al. (2008) and Kyle et al.

(2004; (90,91,93).

In order to be able to understand the principles of BIA, the terms impedance, reactance, capacitance

and resistance need to be explained further: The impedance consists of a combination of R to the

electrical current (caused by the ECW and intracellular water (ICW)) and Xc (Figure 5; (93). R and Xc

are the two variables measured by BIA. Reactance is the reciprocal of capacitance formed by cellular

membranes at low frequencies (94). Cellular membranes act as condensers. If subjected to an

alternating current they are continuously charged and discharged when the current changes its

direction (95). Capacitance is the brief storage of voltage by the condenser, while reactance is the

release of this stored voltage (94).

The relation between the Xc and R is measured by calculating the linear phase angle (PA) (32). The PA

ranges between 0 and 90 degrees (Figure 5). A PA of 0 degrees represents a resistive circuit with no

cell membranes. At 90 degrees a capacitive circuit is present, no fluids and only cell membranes

would be seen. An electrical circuit with a PA of 45 degrees possesses and equivalent amount of R

and Xc. The PA can be calculated directly from the measured values with the following equation:

𝑃𝐴 = (𝑋𝑐 𝑅⁄ ) ∗ 180° 𝜋⁄ . It is an indicator of membrane stability and can be used as a prognostic

value to predict survival of humans with certain diseases, for example liver cirrhosis and cancer (96–

98).

The R is, often combined with morphometric measurements and analysed in multiple mathematical

equations and models to estimate the LBM and FM (41,87). The measured reactance is usually not

implemented in these equations, but used for calculating the PA. Different types of BIA can be

distinguished as reviewed in the humane literature (93). However, only single frequency- and

multifrequency BIA have been used in the cat and will be discussed here.

Single frequency-BIA (SF-BIA) is the method in which the alternating current is sent with a fixed

frequency, usually 50 kHz, through the body (32,87). The obtained values of R and Xc are then

26

analysed using mathematical equations. These equations are empirically made with multiple

regression analysis and mixture theories, often including other body parameters as weight (in cats) or

morphometric measurements or length (in humans) (41,87). With SF-BIA equations estimating TWB

and LBM can be developed by comparing the measurements with DEXA results (41).

Multifrequency-BIA (MF-BIA) measures the impedance (existing of Xc and R) at multiple frequencies.

Because the cellular membranes act as capacitators, very little conduction through the cells is

possible at low frequencies (93,99). Therefore properties of the ECW predominantly determine

conductivity. Cells and their ICW at low frequencies are nonconductive materials. The resistance of

ICW (R0) is thus ideally measured at frequencies lower than 1 kHz (Figure 5 & 6; (99). Conversely, the

influence of the capacitance of the cellular membranes on the impedance (Z) is very small at high

frequencies, causing the electrical current to pass through both the ICW and ECW (99). The TBW

resistance (R∞) is thus best measured at frequencies higher than 5000 kHz (Figure 6; (99). For

technical reasons, measuring values at these extreme frequencies is not possible for impedance

meters, as described in humane literature (90). In cats measurements are therefore usually taken at

50 frequencies between 5 and 1000 kHz (23,92,100). Values of R0 and R∞ are subsequently

extrapolated in an enhanced Cole-Cole model (23,100), as described in a humane article in 1997 (99).

By measuring R, TBW, ICW and ECW can be calculated using an equation from the Hanai mixture

theory (92). This theory describes the conductivity of an electrical current in a suspension of

nonconducting materials (99). Equations to estimate FM, LBM and BF% can also be developed, using

measured R and some morphometric measurements (41,87).

Figure 5: Graph displaying the relationship between the Impedance (z), Phase angle (PA), resistance (R)

and reactance (Xc). R0 represents the resistance of ICW, and R∞ represents resistance of ICW and ECW

(TBW; (92).

.

27

Precision

Repeatability of BIA has been tested by three studies. Cintra et al. (2010) measured for each

electrode SF-BIA values three consecutive times on 20 animals (32). Center et al. (2011) took 9 and 5

MF-BIA measurements in two cats over a period of three days. Center et al. (2013) tested

repeatability of MF-BIA LBM measurements in 11 cats by measuring on two different days.

Reproducibility is difficult to determine, as described below.


The reproducibility of BIA in cats is questionable. Not only is there is no standardized method to

conduct the measurements, but the technique uses multiple systems (SF-BIA, MF-BIA) and different

types of electrodes (32,41,87,92), which poses a challenge for comparing results to reference values

as well as comparing findings of different studies. The use of different types of electrodes, for

example, causes significant differences in the measured values of Xc and R (32). Moreover, different

body postures and BIA configurations can alter the accuracy of the method (87,92), although

differences are not always significant (100).

Figure 6: Conductivity of an alternating electric current through tissues at high and low frequencies. At high

frequencies the capacitance on the cellular membranes on the impedance is small and therefore the

electric current passes through both the intracellular water (ICW) and extracellular water (ECW). At low

frequencies, the cell membranes act as capacitators and inhibit conduction through the cells. All electrical

current will be conducted through the ECW (98).

28


The repeatability of BIA has been tested for both the SF- and MF-BIA methods and can be considered

as good to moderate. Repeatability of measurements is highly dependent on the value that is

estimated and the type of needle that is used. For example, the CV for TBW, EWC, LBM and FM

estimates is reported to be between 2.8% and 16.6% (23,32,101). ICW gives the lowest CV (2.8%-

6.9%), while ECW and FM produces the highest CVs (8.7%-16.6%; (23). Similarly, the type of needle

used as electrode affects the repeatability, with acupuncture needles providing the most stable

results, with a CV of only 0.62% for R measurements (Table 5; (32).

CV R CV Xc CV PA CV LBM CV FM

Adhesive electrodes* 0.62% 0.69% 5.93% 11% -

Acupuncture needle* 0.66% 1.69% 9.99% 6% -

Hypodermic needle* 7.34% 19.83% 29.22% 6% -

Tetrapolar platinum electrode† - - - 6.6-10.1% 6.2-16.6% *Electrodes tested with a SF-BIA method

† Electrode tested with a MF-BIA method

Accuracy

In order to develop reliable mathematical equations that estimate LBM and FM, a few assumptions

have to be made, i.e. 1) the shape of the body is accurately portrayed as 5 cylinders; 2) the

relationship between trunk and leg lengths are constant; 3) the body is euhydrated; and 4) the fat

fraction has a lower water content than the LBM (102). Because of these assumptions, differences in

electrolyte concentrations can influence the BIA results, even without a change in body fluids (93).

Changed electrolyte concentrations will interfere with the ICW-ECW balance, which is exactly what

BIA indirectly measures. However, an abnormal hydration status also changes BIA results (32),

making it difficult to apply BIA in diseased animals, as also seen in humans (103). In human

populations large variations in BIA results can be seen between different populations, because of

differences in body proportions among other things, making wide application of BIA equations

difficult (93). This should be taken into consideration when evaluation achondroplasitic cat breeds.

Both the SF- as the MF-BIA methods have been validated in the cat (32,87,92,100). The reference

methods of choice for validating BIA estimates are chemical analysis, or deuterium dilution (TBW)

and Sodium-bromide dilution (ECW), but the DEXA scan has also been used for validating or

developing FM and LBM estimates (32).

Estimated TBW and LBM by SF-BIA showed excellent correlation with the results from chemical

analysis (r2=0.98 for both; (87). FM and BF% estimated using BIA and multiple morphometric

measurements also resulted in high correlations of 0.93 and 0.82, respectively (87). Equations

derived from SF-BIA results combined with some morphometric measurements can thus accurately

predict TBW, LBM, FM and BF%. However, in the humane literature it is inconclusive if fluctuations in

the ICW can be detected with SF- BIA (91). Also, when compared with DEXA, the equations for LBM

estimates significantly overestimate the LBM (32,87)

Tabel 5: Repeatability of different BIA measurements for various types of electrodes (32,72)

29

With MF-BIA, no significant differences were found for TBW and ECW estimates compared to the

reference methods sodium-bromide dilution and deuterium dilution (r2=0.84-0.86 and r2= 0.74-0.93,

respectively; (23). LBM estimates are also highly correlated with reference methods (r2=0.89; (23) All

6 different configurations for electrode placement, as described by Elliot et al. (2002) were validated

for MF-BIA (92,100). However, some configurations correlate better with reference methods

(92,100). The ‘sternal contralateral path-length’ configuration showed to be best correlated with

deuterium dilution determined TBW (r2=0.84). ECW, as measured by the bromide dilution method,

was best correlated with the ‘sternal body head to tail configuration’ (r2=0.91).

Compared to the BCS, the MF-BIA method appeared better at diagnosing underweight cats than the

BCS system, even though the BCS-system was applied by highly trained observers (23). In a study that

took multiple variables into account, MF-BIA proved to be more useful to estimate lean body mass in

obese animals, while morphometric measurements were found to be more important in leaner

animals (41).


The BIA system is portable, non-invasive and easy to use. Cats generally do not have to be sedated

for measurements, but application of the needles can cause some discomfort and therefore reduce

compliance (23).

Practical applicability of BIA is encountered by certain limitations, because assumptions have to be

made that do not include all breeds. Predictive equations cannot simply be extrapolated to an entire

cat population without taking the assumptions that go along with it into account. As a result, this

restricts the applicability of BIA in the general population, and only enables the equation to be valid

in a population that is similar to the population that was studied.

Moreover, BIA equipment is rather expensive with prices ranging between $3000 and $4000 (104).

Accessories needed and software packages are not included and have to be bought separately which

increases the costs even more. This renders the technique less attractive for use in practice.

30

Ultrasonography Ultrasonography has been used in a single study in cats to estimate total BF% (41). However, in farm

animals, horses and donkeys a lot of research has been conducted into the use of this method to

predict quality of meat and to determine overall body condition (105–108). In dogs few studies have

been performed as well (41,109–111).

General principle

With ultrasonography, depth of the subcutaneous fat layer (SFL) can be measured and used to

subsequently estimate the total BF% of an animal. A great array of different transducers can be used.

Using a high frequency transducer (e.g. 20 MHz) makes it possible to detect smaller variations in the

subcutaneous fat layer (109), but 10 MHz (in dogs) and even 6-8 MHz (in cats) appear sufficient for

this application (41,109). The sole criterion for successful measurements is the use of a linear instead

of a curved transducer. To select the right location for the measurements, the following aspects need

to be taken into consideration: 1) at the location, the SFL has to correlate well with the body

condition; 2) the anatomical location has to be readily identifiable; and 3) the location has to be easy

to reach (110). In dogs, multiple anatomic locations have been tested for predicting the BF% with the

SFL measurements. The lumbar region seems to be the best location. In cats therefore the

subcutaneous fat layer over the 7th lumbar vertebra is measured (41).

Precision

In companion animals, only one research has tested repeatability of ultrasonography measurements

in dogs by taking three measurements of each sonographic image (109). Reproducibility has been

tested in humans, with three unexperienced sonography operators (112). However in the humane

literature concerns about the repeatability and reproducibility of the relatively unknown technique

have been reviewed (113). The interpretation of ultrasonography is more difficult than with different

body condition scoring methods and experience is essential for reliable results.

Reproducibility (Inter-observer variability)

Reproducibility of sonographic SFL measurements is not described for companion animals. However,

supraspinal measurements in humans show great inter-observer correlations with only a CV of 3%

(112). When other measurement locations are considered, CVs range between 1 and 7%. Thus even

with inexperienced operators, reproducibility is high. Although cats do have fur, it can be expected

that CVs will be high, just as in humans.

Despite these promising results, standardization of measurement methods still remains necessary to

enhance the reproducibility of the results in both animals and humans (113). The differences in

normal body composition between cat breeds can also, if not taken into account, increase the

variability of the measurements.

Repeatability (Intra-observer variability)

The experience of the operator plays an important role in the precision, and thus the variability, of

the measurements (113). In dogs, the intra-observer variability of SFL measurements appears to be

high, with CV’s between 33.2% and 51.2% found for three measurements taken of a single

ultrasonography image (109). Measurements on the chest were found to be the most precise

(CV=33.2%), whereas measurement of the SFL on the flank and lumbar region resulted in higher CVs

of 40,8% and 39.6%, respectively.

31

Accuracy

Sonographic measurement of the subcutaneous fat and body condition in dogs was found to be

feasible, whereby the lumbar region, and in particular the L6, L7 and S1 region, was found to be the

preferred anatomical location to measure the SFL for determining body condition or BF%. This was

irrespective of the transducer used (109–111). Estimations of BF% using this technique correlated

well with chemical analysis (r2=0.87) (111). SFL measurements also directly correlated with a 5-point

BCS (r2=0.708; (109). However, the correlation between SFL and BW thus far was found to be

inconclusive, with correlations ranging between 0.60 and 0.80 (109,110).

Since no studies have been performed to validate the use of the technique in cats, the accuracy of

this technique remains unknown. However, one study looked at weight loss in cats, while measuring

their change in body condition with different techniques, among which ultrasonography (41). Weight

loss resulted in an significant reduction of the SFL. Moreover, a good correlation between the

estimates of BF with SFL in the lumbar area and FM as measured by DEXA is recorded.


As ultrasonography is a non-invasive procedure and can be performed without clipping of the fur, it

can generally be performed in the awake animal. Highly motile cats or those that are difficult to

handle might need sedation or anaesthesia in order to complete the measurements.

Since a great number of first line veterinary clinics have an ultrasound machine readily available in

their practice, the threshold of using this technique once properly validated could be low. However,

at this moment in time, in cats but also in dogs, there is insufficient knowledge and research available

to make this technique applicable in clinical practice. First the precision has to be improved and

methods need to be standardized to make the technique reliable.

However, for the owner, the costs of a routine ultrasound may be a reason to decline the use of this

technique to evaluate the body condition of their animal. Moreover, adequate training of the

operator has to be arranged in order to enhance the precision of the methodology.

32

Discussion

When comparing the different methods with each other to evaluate their possibilities for application

in the ferret DEXA and morphometric measurements appear to be the most reliable (Table 6). Other

techniques, such as the relatively new MMS and ultrasonography are promising for scoring the body

condition of cats. However, since both techniques are still under development and not fully validated

yet, their reliability remains uncertain and therefore limit their use in practice for the current time

being. Moreover, each of the techniques has its own additional limitations related to the accuracy of

measurements (i.e. MMS only focuses on the amount of muscle mass of an animal (114,115), and

does not evaluate fat reserves, which comprise an important part of the animal’s total body

condition whereas ultrasound only evaluates the subcutaneous fat reserves and not the muscle

mass), costs and time associated with the measurements (particularly for ultrasonography), and

necessity for sedation (particularly non-compliant animals). Sonography might pose extra difficulties

in the ferret related to their small body size and active character. Sedation will therefore often be

necessary. The MMS, in contrary, should be easily applicable in ferrets also. But as described above,

no full validation is present for either techniques.

Of the various methodologies that exist, BIA is the only method that in its present form does not

appear useable in the veterinary practice. Although BIA can have great accuracy, it’s precision is

often too variable for reliable application in practice. For ferrets, this would be no different.

DEXA and morphometric measurements appear to be the most reliable methods for estimating LBM,

FM and BF%. Both methods are very precise and show high correlations with their reference

methods (33,36). DEXA is considered the gold standard in alive animals, even though errors in the

estimation of BF% for the individual can be high (29,33). DEXA results are also less dependent on

different body morphologies, in contrast to morphometric measurements, which necessitate

separate equations to be made for specific subpopulations to enable accurate estimations to be

made for breeds with distinct body morphologies. As such, this system is less easily applied in

practice, especially because of time constraints on the consultation, which do not allow much time to

be spent on the different measurements. In ferrets, being very active animals, these time constraints

will make it difficult for tape measurements to be taken precisely. However, great reproducibility of

body measurements in ferrets has been reported (116), suggesting this method should be applicable.

Although accuracy is lower, BCS systems in general are considered reliable as well and because of

their practical applicability, these are used most commonly in practice. For use in the ferret this

technique is very promising. Veterinarians are already accustomed to the technique and because of

low time requirements, compliance in the ferrets can be more easily assured. However, precision and

accuracy differ between BCS systems, with the 9-point system showing the highest correlations with

DEXA BF% estimates (59). Since training and experience have been found to significantly influence

the reproducibility of results, this system is best applied by an experienced veterinarian. For less

experienced staff and owners, the S.H.A.P.E. BCS system, which was also found to have good

reliability, can provide a suitable alternative (70). However, even though a 5-7% increase in BF% per

step (9-point system) or half-step (5-point system) is reported, making predictions of BF% based

solely on an animals BCS is highly inaccurate (59,67). The differences between BF% in the various

categories are often nonsignificant and overlap between categories is frequently seen (25,59,67). In

addition, BF% within BCS categories have been found to differ greatly between study populations,

activity level and sex (25,59,67). Thus, although the BCS systems can be very useful for estimating the

body condition of an animal, specific estimates of BF% or total LBM cannot be made with this

technique.

33

Conclusion Based on the findings of this literature study, the objective and easily reproducible morphometric

measurements seems to be the perfect combination between quick, cheap assessments and

objective and reliable results. Moreover, with morphometric measurements estimates of LBM and

FM% can also be made, which is not possible when using a BCS system. However, BCS systems can be

used as a good substitution for morphometric measurements, especially when inadequate data is

available for developing reliable morphometric equations. Morphometric measurements and BCS

systems are thus the best options for evaluating the body condition of ferrets in the basic veterinary

practice. Therefore, in the individual project, a combination of these techniques will be use to

developed a system for the evaluation of a ferret’s body condition.

34

Table 6: An overview of the body condition scoring methods reviewed

Po

ssib

iliti

es

for

the

fe

rre

t

Wh

en u

sin

g p

aed

iatr

ic o

r sm

all a

nim

al

soft

war

e, t

he

DEX

A s

can

can

be

eas

ily

use

d f

or

ferr

ets.

A b

od

y co

nd

itio

n s

core

sys

tem

fo

r th

e

ferr

et c

an b

e d

evel

op

ed a

nd

is e

asily

usa

ble

.

Wh

en, a

fter

mo

re r

ese

arch

, th

e

relia

bili

ty o

f th

is t

ech

niq

ue

is p

rove

n,

it c

ou

ld b

e d

evel

op

ed t

o s

up

ple

men

t th

e B

CS

in f

erre

ts.

Ferr

ets

are

ve

ry a

ctiv

e an

imal

s,

mak

ing

the

mea

sure

me

nts

a

chal

len

ge. H

ow

ever

, it

is n

ot

imp

oss

ible

an

d t

he

tech

niq

ue

is v

ery

relia

ble

.

Fixa

tio

n w

ith

ou

t se

dat

ion

of

the

ferr

et

can

be

dif

ficu

lt w

hen

exe

cuti

ng

this

tech

niq

ue.

Pat

ien

t co

mp

lian

ce c

an b

eco

me

an

issu

e, b

ut

it s

ho

uld

be

po

ssib

le t

o u

se

this

tec

hn

iqu

e, if

pro

per

ly v

alid

ated

.

Dis

adva

nta

ges

Cro

ss c

alib

rati

on

is n

ece

ssar

y fo

r

com

par

ing

resu

lts

bet

we

en d

iffe

ren

t

den

sito

met

ers.

Se

dat

ion

is n

eed

ed t

o p

erf

orm

th

e

scan

an

d t

he

scan

s ar

e re

lati

vely

exp

ensi

ve.

The

mea

sure

men

ts a

re s

ub

ject

ive

and

trai

nin

g is

req

uir

ed

.

The

accu

racy

of

this

met

ho

d is

on

ly

mo

der

ate

and

ob

serv

atio

ns

are

sub

ject

ive.

Equ

atio

ns

mad

e ar

e o

nly

re

liab

le f

or

a

po

pu

lati

on

sim

ilar

to t

he

stu

dy

po

pu

lati

on

. Mu

ltip

le m

easu

rem

en

ts

are

usu

ally

nec

essa

ry a

nd

an

imal

s ca

n

bec

om

e in

tole

ran

t to

han

dlin

g.

As

wit

h D

EXA

, dif

fere

nce

s in

elec

tro

lyte

co

nce

ntr

atio

ns,

or

hyd

rati

on

(o

edem

a) in

terf

ere

wit

h t

he

resu

lts.

Als

o, a

pp

licat

ion

of

the

nee

dle

s ca

n

cau

se s

om

e d

isco

mfo

rt.

The

ult

raso

un

d t

akes

, co

mp

ared

to

the

oth

er t

ech

niq

ues

, lo

nge

r an

d is

rela

tive

ly e

xpen

sive

. Th

e SF

L m

easu

rem

ents

are

no

t va

lidat

ed in

cats

, an

d r

epea

tab

ility

is c

urr

entl

y lo

w.

Ad

van

tage

s

This

tec

hn

iqu

e is

co

nsi

der

ed t

he

gold

en s

tan

dar

d f

or

mea

suri

ng

LBM

,

FM a

nd

BF%

. It

give

s an

pre

cise

an

d

accu

rate

est

imat

ion

of

an c

ats

bo

dy

com

po

siti

on

.

Thes

e sy

stem

s ar

e ch

eap

, no

n-i

nva

sive

and

eas

y to

use

.

This

to

ol c

an h

elp

dia

gno

se m

usc

le

was

tin

g, a

n im

po

rtan

t si

gn o

f d

isea

se.

It is

no

n-i

nva

sive

an

d a

ids

in t

he

do

cum

enta

tio

n o

f vi

sib

le c

han

ges

in

mu

scle

mas

s.

The

mea

sure

men

ts a

re n

on

-in

vasi

ve

and

litt

le t

rain

ing

is n

eed

ed t

o m

ake

them

rel

iab

le. N

o e

xpen

sive

eq

uip

men

t o

r lo

ts o

f sp

ace

are

req

uir

ed.

The

LBM

an

d F

M c

an b

e es

tim

ate

d

wit

ho

ut

the

nee

d o

f an

aest

hes

ia, a

nd

the

dev

ices

are

po

rtab

le.

Ult

raso

un

d m

ach

ines

are

rea

dily

avai

lab

le in

vet

erin

ary

clin

ics

and

ult

raso

no

grap

hy

is a

no

n-i

nva

sive

p

roce

du

re.

Re

liab

ility

DEX

A r

esu

lts

are

rep

eata

ble

on

th

e

sam

e d

en

sito

met

er,

bu

t

rep

rod

uci

bili

ty is

low

. No

rmal

, co

nst

ant

tiss

ue

hyd

rati

on

is v

ery

imp

ort

ant

for

acq

uir

ing

relia

ble

resu

lts.

Ho

wev

er, D

EXA

is v

ery

accu

rate

an

d c

on

sid

ere

d a

go

lde

n

stan

dar

d f

or

mea

suri

ng

BF%

.

Trai

nin

g is

ne

ede

d t

o m

ake

the

BC

S m

ore

rel

iab

le, b

ut

corr

ela

tio

ns

wit

h o

ther

ob

serv

ers

and

DEX

A

are

gen

eral

ly h

igh

.

The

MM

S is

sti

ll u

nd

er

dev

elo

pm

en

t an

d o

nly

mo

der

ate

ly

rep

rod

uci

bili

ty is

see

n. I

t’s

relia

bili

ty is

sti

ll u

nce

rtai

n.

Mo

rph

om

etri

c m

easu

rem

en

ts a

re

in g

en

eral

rea

lly r

elia

ble

, bu

t ca

re

mu

st b

e ta

ken

wh

en a

pp

lyin

g th

e

equ

atio

ns

to a

po

pu

lati

on

dif

fere

nt

fro

m t

he

stu

dy

po

pu

lati

on

.

BIA

has

be

en v

alid

ate

d in

do

gs a

nd

cats

, bu

t n

o s

tan

dar

diz

ed

met

ho

ds

are

avai

lab

le a

nd

th

e re

pro

du

cib

ility

is q

ues

tio

nab

le.

Incl

ud

ing

mo

rph

om

etri

c

mea

sure

men

ts m

akes

BIA

mo

re

accu

rate

.

The

relia

bili

ty f

or

this

tec

hn

iqu

e in

cats

is f

or

the

big

gest

par

t

un

kno

wn

, bu

t th

e ex

per

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35

Part 2: Individual project. Evaluating the body condition of the ferret

The BCS-system is very popular and is used with great success in dogs and cats. The ease of use and

the lack of tools needed, makes it a very attractive method. Therefore the aim of this research is to

develop a body condition scoring method for ferrets resembling a BCS-system, if needed

supplemented with morphometric measurements. To validate the techniques, DEXA scans would

ideally need to be performed as these are considered the gold standard. Within the time frame of

this study, this was not found feasible, and therefore considered to be beyond the scope of the

project.

Material and Methods

The animals:

41 ferrets, 19 males, 22 females, from different ferret shelters2 (37 animals) and private owners3 (4

animals) were enrolled in the study. In addition, 3 ferret patients from the ‘Veterinary Ferret Clinic’

(Reygerboslaan 32, Giessen, Netherlands) were examined. Females weighed 759g ± 219 g (515-

1385g) and males 1317 ± 339 g (828-2390g). Animals were aged 3.5 years ± 1.8 (6 months – 7 years)4.

Among other characteristics collected were age, gender, body weight (BW) and castration status. All

ferrets, except three, were surgically neutered. Of these three animals (one male and two females),

two animals (the females) had received an implant, resulting in only one intact animal (a male ferret)

included in the study. Most ferrets were reported to be generally healthy, but an enlarged spleen,

chronic bronchitis, kidney tumours, stomach problems and middle ear infections were seen.

There were no specific inclusion criteria’s for ferrets entering the study; however severely ill animals

or animals that appeared to be in pain were not included in this study. In order to increase the

ferrets’ compliance with the measurements, ferrets were provided with treats, vitamin paste or

convalescence support (Royal Canin, Poort van Veghel 4930, Veghel, Netherlands).

Dependent on de shelter, the ferrets were housed together in groups of two or three (foundation

‘Frettig Gestoord’) or individually (foundation ‘De Fret’, foundation ‘Fret & Welzijn’). In most cases,

ferrets were housed indoors in a ferret room where most of the ferret cages and play areas were

stationed to enable visual, auditory and/or olfactory contact. In one shelter, ferrets were housed

outside in the garden.

Data was collected in March and April 2017. The measurements performed were non-invasive and

caused no discomfort to the animals. All caregivers gave informed consent before animals were

measured.

2 three Ferret shelters of foundation “Frettig Gestoord”: Ilonka van Lieshout, Rine Oddens and Stephenie Baas, a Ferret shelter of foundation “De Fret”: Marianne Boymans and a ferret shelter from Foundation “Fret & Welzijn”: Chaimel Lerou were visited. 3 Of these four privately owned ferrets, three were housed together in one cage, while the other ferret was housed alone with a different owner. 4 For many ferrets, the age has been estimated by the shelters.

36

Study design

To develop the body condition scoring system, all ferrets were photographed, visually inspected,

palpated, and weighed. Moreover, morphometric measurements were taken to evaluate the animals

body condition. All procedures were carried out by one researcher (I.B.), whereby results were noted

on two separate forms: one for the inspection and palpation of bodily structures and one for the

morphometric measurements (Appendix 6 and 7).

Because of their experience with ferrets , the ferret owners, caregivers of the shelter ferrets in the

shelters, and veterinarian were also questioned about their opinion of the animals’ body condition.

Based on their evaluations, the ferrets were divided into three categories: underweight – optimal

weight – overweight. Next, the body condition was evaluated by the researcher (I.B.) based on the

visibility and palpability of specific anatomic structures. Similarly, morphometric measurements and

body weight were collected. These aforementioned measurements were subsequently statistically

36nalysed to determine whether and which variables correlated best with the body condition as

identified by the experts. For the variables that correlated well with the body condition, it was also

determined which description was most often chosen for a particular body condition. These

description were taken up into the BCS-system chart to complement the photographs.

Photographs

To develop the BCS-chart, lateral and dorsal photographs were taken of 44 ferrets in different body

conditions using a 14.0 megapixels Nikon Coolpix s3100 camera. To even out influence of the

background, a green screen cloth was used as background.

Weight

Bodyweight was collected from all ferrets. The type of scale that was used depended on the type that

was available in the shelter or home of the ferret. As a result, a mixture of , one analogous- and four

digital kitchen scales, as well as two advanced baby scales were used. All scales could at least weigh

accurately in grams and were tarred before use. Therefore, weight measurements were collected in

grams.

Visual inspection

Ferrets were visually inspected for visibility of the cervical and lumbar vertebrae, the tuber ischium,

ribs and waist, as described for de BCS in cats (59,67,70). The visibility of each of these bony

structures and presence of a waist were scored on a three point scale, i.e. clearly visible – partially

visible – not visible.

37

Palpation of anatomical structures

The following bony structures were evaluated for their palpability: cervical and lumbar vertebrae,

tuber ischium, ribs, waist and paws (see also Table 7 for a detailed description, methodology and

grading) Ribs were additionally evaluated for an estimation of the thickness of the fat layer on the

ribs. Moreover paws were graded for the amount of muscle and/or fat present, whereas the

abdomen was palpated on visceral fat content and alignment with the thorax, whereby the amount

of visceral fat present was estimated in centimetres. Last, muscle mass was graded by palpating the

m. longissimus and m. transversospinalis of the lumbar region of the back and the m. biceps femoris

of the hind limb (Table 7).


Five morphometric measurements were taken of each ferret, i.e. ventral body length (VBL), dorsal

body length (DBL), the leg index measurement (LIM), ribcage circumference (RC) and belly

circumference (BC) (71,116). To obtain the measurements, a tailor’s measuring tape with a

centimetre scale (accurate up to 1mm) was used. All measurements were collected once from each

ferret by the same researcher (I.B.), while the ferret was being scruffed, held in a vertical or

horizontal position by the caregiver as depicted in Figure 7 (Figure 7, Table 7).

Data analysis

Data analysis was performed using the program R-studio (open source desktop version 0.98.1062) 5.

Univariate linear regression models were used to test morphometric measurements for correlations

with each other, BW, body condition and gender. Moreover, body condition was tested for

correlations with age, BW and gender.

In order to make preselection of the variables to be tested for collinearity, a Fisher’s exact test for

each of the variables was performed. This test was chosen because the relatively small dataset in

combination with the large number of variables resulted in small numbers of animals per category.

To facilitate interpretation of the Fisher’s exact test and logistic regression, the continuous variables

(i.e. BW and morphometric measurements) were converted into categorical variables by dividing

these into three groups whereby the lower third of ferrets with the lowest score received the label

small/light, the middle third of ferrets received the label average and the top third received the label

large/heavy. For all the variables with p<0.2 in the Fisher exact test, the Odds-ratio (OR) and 95%

confidence interval (95% CI) were determined using univariate analysis. Hereby making a selection

for which variables can be used in an logistic regression. For some of the variables no animals fulfilled

the criteria for a specific category. In order to still enable a reasonable estimation to be made of the

OR and its 95% CI, the OR was manually calculated by adding 0,5 to all four fields of the table, after

which the 95% CI was determined (see Box 2).

The dependent variable ‘body condition’ was coded into dummy variables: obese, optimal weight

and underweight. With these three dummy variables and preselected variables from the Fisher exact

test, the five best logistic regression models were found by fitting all the possible models, using the

package glmulti (117). From these five best models, the most suitable model for each dummy

variable was chosen. These three final models were subsequently implemented in the BCS-chart.

5 www/rstudio.com

38

A B

C

Figure 7: Photographs representing the way the ferrets were hold during morphometric measurements.

The photographs are not owned by the author and have been taken from the internet (117–119).

.

39

Box 2: Formulas used for the manual calculation of OR and its 95% CI

Table 7: Detailed description of the various structures that were visually inspected and/or palpated as well as

the morphometric measurements that were obtained during this study. Accidental missing data is reported as

‘unknown’.

Variable Description of the measurement Grading

Visual inspection

Visually inspection took place of the following structures: cervical and lumbar vertebrae, the tuber ischium, ribs and waist.

Clearly visible

Partially visible

Not visible

Palpability of the cervical and thoracic vertebrae

Palpability of the cervical and thoracic vertebrae was performed while holding the ferret on one hand and palpating with the other.

Very easily palpable

Easily palpable

Palpable with some pressure

Hardly/not palpable

Palpability of the ischial tuberosity

The ischial tuberosity was palpated while the ferret was standing on the table.

Easily palpable


Rib fat coverage

With the ferret standing on a table, the rib fat coverage was estimated by sliding both hands simultaneously across both sides of the ribcage and estimating the amount of fat covering the ribs.

No fat coverage

Scant fat coverage

Little fat coverage

Moderate fat coverage

Substantial fat coverage

Unknown

Palpability of the ribs

Palpability of the ribs was determined with the ferret standing on a table while the observer slided both hands simultaneously over the ribcage. Note: In contrast to rib fat coverage this variable provided an estimation of the palpability of the ribs rather than an estimation of the fat coverage over it.


Easily palpable


Hardly/not palpable

𝑂𝑅 = 𝑐/𝑑

𝑎/𝑏

ln(CI) = ln(OR)±1,96 * 𝐿𝑁(𝐶𝐼) = 𝐿𝑁(𝑂𝑅) ± 1.96 ∗ √1

𝑎+

1

𝑏+

1

𝑐+

1

𝑑

with

obese/optimal/underweight The rest

Variable level 1 (ref) a b

Variable level 2 c d

Etc.

40

Palpability of the waist

The waist was palpated by sliding both hands from the ribs dorsocaudal to feel the waist.


Easily palpable


Hardly/not palpable

Palpability of the lumbar vertebrae

The lumbar vertebrae were palpated by taking the ferret up in one hand and feeling along the spine with the other.


Easily palpable


Hardly/not palpable

unknown

Shape of the abdomen

The shape of the abdomen was determined primarily based on palpating the transition from ribcage to abdomen. I

Tucked in abdomen, whereby the abdomen is of a smaller height than the thorax

Normal abdominal shape, whereby the abdomen is of similar height as the thorax

Distended abdomen, whereby the height of the abdomen is larger than the height of the thorax

Estimation of abdominal fat content

An estimation of abdominal fat content was made by holding the ferret in one hand and palpating the subcutaneous fat at the abdominal level with the other hand.

< 1 cm

≥ 1cm

Unknown

Palpability of the bones in the front paws

With the ferret standing on the table, the palpability of the radius and ulna was determined by feeling both front paws simultaneously whereby the front leg was palpated between thumb and index/middle finger.

Easily palpable Palpable with some pressure Unknown

Estimation of muscle and fat tissue in the front paws

The amount of muscle and fat covering the radius and ulna was determined similar to the palpability of the bony structures of the front leg

No fat present Scant fat/muscle coverage Little fat/muscle coverage Moderate fat/muscle coverage Substantial fat/muscle layer Unknown

Muscle evaluation

The muscle mass of the ferrets was evaluated by palpating the m. longissimus and m. transversospinalis in the lumbar region and by palpating the m. biceps femoris of both hindlegs.

Normal Slight muscle wasting Severe muscle wasting

LIM* The leg index measurement was determined by measuring the distance between the patella and the calcaneus tuber of one of the hindlimbs. Which hind limb was measured, was dependent on the way the owner presented the ferret to the caregiver (Figure 7A & B).

Small o female<6 cm o male<7.5 cm

Average o female: 6-7 cm, o male: 7.5-8.4 cm

Large o female>7 cm

male>8.4 cm)

41

DBL* Dorsal body length was measured from the tip of the nose to the tale base, while the ferret was being scuffed or held in a vertical position by the caregiver (Fig7 A & B).

Small o female<34.5 cm o male<42.9 cm

Average o female: 34.5-37 cm o Male: 42.9-44.9 cm

Large o female>37 cm

male>44.9 cm VBL* Ventral body length was measured

from the tip of the nose to the anus, across the ventral side of the animal, while the caregiver holds the animal in vertical position.

Small o female<33.0 cm o male<39 cm

Average o female: 33-4 cm o male: 39-42.9 cm

Large o female>36 cm

male >42.9 cm RC* The ribcage circumference was

measured with the ferret held in vertical or horizontal position by the caregiver. The measurements were taken at the xyphoid process.

Corrected for gender: Small

o female <17.0 cm o male <20 cm

Average o female: 17.0-18.2 cm o male: 20-21.9 cm

Large o female:> 18,3 cm o male >21.9 cm

BC* The belly circumference was measured at the widest point of the belly, with the ferret being hold in a vertical or horizontal position by the caregiver.

Small o female<18.5 cm o male<23.9 cm

Average o female: 18.5-20.9 cm o male: 23.78-25.5 cm

Large o female>20.9 cm

male>25.5 cm BW* The body weight of the ferrets was

measured in grams using a kitchen scale (analogous or digital) or a professional baby scale.

Light o female<637 g o male<1248 g

Average o female: 637-716 g o male: 1248-1404 g

Heavy o female>716g o male>1404 g

*These variables received a correction for the gender influence on body size. See results for more

explanation.

42

Results

Of the 41 ferrets, 9 were classified as overweight, 20 as having an optimal body condition and 12 as

being underweight.

Visual inspections

The extensive amount of fur that the ferrets possessed hindered visual inspections of the body

conditions. Only in one animal that was partially bald because of an endocrine disorder, the lumbar

vertebrae, waist and the ischial tuberosity were partially visible. As a result, this data was not further

analysed.

Palpations

All ferrets were compliant with the palpations and in most ferrets all variables were evaluated.

However, in some animals a variable was forgotten or not registered. These missing values were

assigned to the category ‘unknown’ (Table 7). For all anatomical structures that were palpated, a

significant correlation with body condition score was found (Fisher’s exact test; p<0.2; Table 8).

The palpability of the cervical and thoracic vertebrae showed a clear distinction between obese,

optimal and underweight animals (p=0.002). Overweight animals were most likely to be scored

‘hardly/not palpable’ (OR=1.29; CI=0.04-38)), while ferrets in optimal body condition were 9 times

more likely to be scored ‘easily palpable’ than ‘very easily palpable’ (OR=9; CI=0.2-363). Underweight

animals, in contrast, were most likely to be scored ‘very easily palpable’. Similarly, in most

overweight ferrets and those in optimal condition, the ischial tuberosity was palpable with some

pressure (OR=1.87; CI=0.2-23 and OR=2.22; CI=0.2-27, respectively). In underweight ferrets, the

ischial tuberosity was 3.3 times more likely to be easily palpated than to be only palpable with some

pressure (p=6.90E-07, OR=0.3; CI=0.01-6).

Overweight animals were most likely to have a considerable (moderate) amount of fat covering their

ribs (p<0.002, OR=7.61; CI=0.3-175, compared with no fat coverage), whereas ferrets in optimal body

condition had the highest odds for a small amount of fat coverage (OR=9; CI=0.8-108) and

underweight ferrets most often had no fat covering their ribs (all OR<1, compared to no fat

covering).

Upon evaluation of the palpability of the ribs themselves, overweight animals were found 21 times

more likely to have hardly or non-palpable ribs (p=0.18, OR=21; CI=0.2-2860), whereas the ribs of

ferrets in optimal condition were generally easily palpable (OR=2.67; CI=0.2-34) and very easily

palpable in underweight ferrets (all OR<1, compared to ‘very easily palpable’). Similar findings were

observed for palpability of the lumbar vertebrae (p=1.43E-05, ORoverweight=12; CI=0.-422 and ORoptimal

weight=5; CI=0.4-72), with the only exception that the lumbar vertebrae of animals in optimal body

condition were most likely to be palpable with some pressure (Table 8).

In contrast to the other parameters, the palpability of the waist revealed no obvious distinction between the different body condition groups, i.e. ferrets in all body conditions were found just as likely to have a very easily palpable waist as a hardly/not palpable waist (p=5.31E-07). Nevertheless, overweight animals were found 7 times more likely to have their waist palpable with some pressure rather than very easily palpable (OR=7; CI=0.2-291) while in underweight ferrets the waist was most likely to be easily palpable (OR=1.6; CI=0.1-42, compared to very easily palpable)). Abdominal shape, on the other hand, was found to be more informative, with obese ferrets being 6.3 times more likely to have an extended abdominal and 0.3 times more likely to have an normal abdominal shape than an tucked in abdomen (p=3.44E-11). For ferrets in optimal body condition, the OR for these

43

categories were 12.8 (CI=8-17) and 7.9 (CI=-1-5), respectively. In contrast, underweight ferrets were 7.7 times more likely to have a tucked in abdomen than a normal-sized abdomen (CI=0.4-167), compared to ferrets that are not underweight. The estimation of abdominal fat content and palpability of the bones in the front paws show similar

patterns. Obese ferrets and ferrets in optimal condition are 12 times and 1.9 times more likely to

have >1cm abdominal fat, respectively (p=0.001, CI=3.3-43 and CI=0.4-8.6). Underweight animals

most often have <1 cm of abdominal fat (Table 8). However, the bones in the front paws are only

palpable with some pressure in the group of obese ferrets (p=9.92E-08, OR=18; CI=3-115).

For the front legs, the amount of muscle and fat tissue were divided into 5 categories resulting in

very few ferrets being classified in each category. Nevertheless, a clear shift between the ORs in the

different body condition groups could be seen, with the obese group being 15 times more likely to

have a substantial fat/muscle layer and the underweight animals 9 times more likely to have scant

fat/muscle coverage (p=0.045).

Univariate analysis of the muscle evaluation revealed that underweight animals are more prone to be

suffering from muscle wasting than other animals, with them having 5.06 times more change of

having slight muscle (CI=-0.3-3.58) wasting and being 9,71 more likely to have strong muscle wasting

compared with underweight ferrets with normal muscle mass (CI=-1-5.6), while the obese animals

and ferrets in optimal condition have very low ORs for muscle wasting compared to normal muscle

mass (OR=0.003-0.93)

44

variables category

Overweigt (n=9)

optimal body condition (n=20)

underweight (n=12)

p-value Fishers exact test

% (n) % (n) % (n)

palpability of the cervical and thoracal vertebrae

very easily palpable 0 0 8.3 (1) 0.002

easily palpable 0 20 (4) 8.3 (1) palpable with some pressure 55.5 (5) 45 (9) 58.3 (7)

hardly/not palpable 44.4 (4) 35 (7) 25 (3) palpability of the ischial tuberosity easily palpable 88.9 (8) 90 (18) 100 (12) 6.90E-07

palpable with some pressure 11.1 (1) 10 (2) 0 (0)

rib fat coverage no fat present 0 (0) 5 (1) 33.3 (4) 0.002

scant fat/muscle coverage 11,1 (1) 10 (2) 33.3 (4)

little fat/muscle coverage 22.2 (2) 45 (9) 16.7 (2)

moderate fat/muscle coverage 44.4 (4) 20 (4) 16.7 (2)

substantial fat/muscle layer 11.1 (1) 5 (1) 0

unknown 11.1 (1) 15 (3) 0

palpability of the ribs very easily palpable 0 (0) 5 (1) 16.7 (2) 0,18

easily palpable 22.2 (2) 60 (12) 58.3 (7)


hardly/not palpable 11.1 (1) 0 (0) 0 (0)

palpability of the waist very easily palpable 0 (0) 5 (1) 0 (0) 5.3E-07

easily palpable 66.7 (6) 85 (17) 100 (12)


hardly/not palpable 0 (0) 5 (1) 0 (0)

palpability of the lumbar vertebrae very easily palpable 0 (0) 5 (1) 16.7 (2) 1.4E-05

easily palpable 33.3 (3) 40 (8) 75 (9)

palpable with some pressure 33.3 (3) 50 (10) 8.3 (1)

hardly/not palpable 22.2 (2) 5 (1) 0 (0)

unknown 11.1 (1) 0 (0) 0 (0)

Shape of the abdomen tucked in abdomen 0 (0) 0 (0) 33.3 (4) 3.4E-11

Normal abdomen 0 (0) 35 (7) 66.7 (8)

Distended abdomen 100 (9) 65 (13) 0 (0)

estimation of adominal fat content <1 cm 0 (0) 20 (4) 58.3 (7) 0.001

≥ 1 cm 77.8 (7) 55 (11) 25 (3)

unknown 22.2 (2) 25 (5) 16.7 (2) palpability of the bones in the front paws easily palpable

77.8 (7) 20 (4) 8.3 (1) 9.9E-08

Table 8: Correlations of BCS variables with body condition, Fisher exact test

45

palpable with some pressure 22.2 (2) 75 (15) 91.6 (11)

unknown 0 (0) 5 (1) 0 (0) estimation of muscle and fat tissue in the front paws

no fat present 0 (0) 5 (1) 0 (0) 0.045

scant fat/muscle coverage 0 (0) 5 (1) 33.3 (4)

little fat/muscle coverage 11.1 (1) 35 (7) 41.7 (5) moderate fat/muscle coverage 22.2 (2) 5 (1) 0 (0)

substantial fat/muscle layer 22.2 (2) 0 (0) 0 (0)

unknown 44.4 (4) 50 (10) 25 (3)

muscle evaluation normal 100 (9) 90 (18) 66.7 (8) 9.1E-03

slight muscle wasting 0 (0) 10 (2) 25 (3)

strong muscle wasting 0 (0) 0 (0) 8.3 (1)

variables category

Overweight (n=9)

optimal body condition (n=20)

underweight (n=12)

p-value Fishers exact test

LIM small average large

22.2( 2) 33.3 (3) 44.4 (4)

30 (6) 25 (5) 45 (9)

8.3 (1) 66.7 (8) 25 (3) 0.23

DBL small average large

11.1(1) 22.2(2) 66.7(6)

45(9) 25(5) 30(6)

25 (3) 41.7 (5) 33.3 (4) 0.28

RC small 11.1 (1) 20 (4) 58.3 (7) 0.01

average 11.1 (1) 50 (10) 25 (3)

large 77.8 (7) 30 (6) 16.7 (2) BC small 0 (0) 25 (5) 66.7 (8) 0.02

average 33.3 (3) 35 (7) 16.7 (2)

large 66.7 (6) 40 (8) 16.7 (2) BW light 44.4 (4 25 (5) 58.3 (7) 0.05

average 44.4 (4) 15 (3) 25 (3)

heavy 11.1 (1) 60(12) 16.7(2)

Table 9: Correlations of morphometric measurements with body condition, Fisher exact test. All measurements are

corrected for the gender influence on body size.

46

Table 10: Univariate analysis of the BCS variables with p<0.2 in the Fisher exact test

variables categories overweigt vs the rest optimal vs the rest

underweight vs the rest

OR 95% CI OR 95% CI OR 95% CI

palpability of the cervical and thoracal vertebrae

very easily palpable 1 ref 1 ref 1 ref

easily palpable 0.3* 0.004-20.4* 9* 0.2-362.5* 0.2 0.01-7.4 palpable with some pressure 1* 0.04-28.3* 2.3* 0.08-62.4* 0.5 0.09-2.7

hardly/not palpable 1.3* 0.04-8.0* 3* 0.1-86.1* 0.3 0.01-5.8 palpability of the ischial tuberosity

easily palpable 1 Ref 1 Ref 1 Ref palpable with some pressure 1.9 0.15-23.4 2.2 0.2-26.6 0.3* 0.01-6.3*

rib fat coverage

no fat present 1 Ref 1 Ref 1 Ref scant fat/muscle coverage 2.5* 0.09-75.8* 1.6 0.1-24.7 0.3 0.02-4.7 little fat/muscle coverage 2.4* 0.1-58.8* 9 0.8-108.3 0.05 0.003-0.7 moderate fat/muscle coverage 7.6* 0.3-175.0* 2.7 0.2-33.5 0.06 0.004-0,9

substantial fat/muscle layer 11* 0.3-433.8* 4 0.1-137.0 0.07* 0.002-2.3*

unknown 4.7* 0.15-151.5* 12 0.5-280.1 0.04* 0.001-1.2*

palpability of the ribs

very easily palpable 1 Ref 1 Ref 1 Ref

easily palpable 0.9* 0.4-23.0* 2.7 0.2-34.2 0.3 0.02-3.3 palpable with some pressure 4.3* 0.2-98.2* 1.6 0.1-20.9 0.1 0.01-1.7 hardly/not palpable 21* 0.2-2859.8* 0.6* 0.02-34.2* 0.2* 0.01-8.8*

palpability of the waist

very easily palpable 1 Ref 1 Ref 1 Ref

easily palpable 0.6* 0.02-18.1* 0.3* 0.10-1.0* 1.6* 0.06-42.1* palpable with some pressure 7* 0.2-291.4* 0.1* -5.7-1.8* 0.3* 0.004-25.4*

hardly/not palpable 1* 0.01-92.4* 1* 0.01-92.4* 1* 0.01-92.4* palpability of the lumbar vertebrae

very easily palpable 1 Ref 1 Ref 1 ref

easily palpable 1.4* 0.06-33.6* 1.3 0.1-17.3 0.4 0.03-5.3

palpable with some pressure 2.1* 0.09-52.0* 5 0.4-71.9 0.04 0.002-0.9

hardly/not palpable 11.7* 0.32-422.2* 1 0.03-29.8 0.1* 0.002-3.1*

unknown 21* 0.15-2859.8* 0.6* 0.01-24.5* 0.2* 0.005-8.8* abdominal shape

tucked in abdomen 1 Ref 1 Ref 1 Ref

normal abdominal shape 0.2* 0.1-0.8* 7.9* -1.0-5.2* 0.1* 0.006-2.8*

distended abdomen 6.3* 0.3-132.1* 12.8* 9.8-15.8* 0.002* 0.00004-0.1*

estimation of adominal fat content

<1 cm 1 Ref 1 Ref 1 Ref

≥ 1 cm 11.9* 3.3-43.5* 1.9 0.4-8.6 0.01 0.02-0.5

unknown 7.7* 0.3-183.0* 2.2 0.4-13.2 0.2 0.02-0.2 palpability of easily palpable 1 ref 1 Ref 1 Ref

47

the bones in the front paws

palpable with some pressure 18.2 2.9-114.6 004 0.1-1.8 0.1 0.02-1.3

unknown 0.2* 0.004-14.3* 5.7* 0.1-336.2* 2.6* -2.7-4.6* estimation of muscle and fat tissue in the front paws

no fat present 1 Ref 1 Ref 1 Ref scant fat/muscle coverage 0.3* 0.004-2.4* 0.1* 0.003-4.5* 9* 0.22-362.5* little fat/muscle coverage 0.4* 0.005-25.8* 0.4* 0.01-11.2* 1.9* 3..4* moderate fat/muscle coverage 5* 0.1-220.6* 0.2* 0.005-8.8* 0.4* 0.01-33.6*

substantial fat/muscle layer 15* 0.2-1236.3* 0.07* 0.0008-5.5* 0.6* 0.01-49.5*

unknown 1* 0.03-29.2* 0.5* 0.02-13.1* 0.7* 0.02-21.9*

muscle evaluation

normal 1 Ref 1 Ref 1 Ref

slight muscle wasting 0.003* 0.0001-0.1* 0.6 -2.4-1.5 5.1 -0.3-.6

strong muscle wasting 0.9* 0.03-24.8* 0.3* 0.01-8.3* 9.7* -1.0-5.6*

*values that are manually calculated with the formulas shown in box 2


VBL, DBL and LIM were found to be moderately correlated with each other and BW in univariable

regression analysis (r2=0.34-0.72; p<0.05). However, low, insignificant correlations between these

measurements and the body condition of the ferrets were found (r2=0.01-0.10). The Fishers exact

test showed similar results, with p-values found between 0.28 and 0.76 (Table 9 & 11).

Gender and body size (VBL, DBL and LIM) showed an obvious, but moderate, correlation (Figure 9),

with males clearly having higher values for these measurements (r2=0.44-0.61, p<2.06E-06 for all

three body size measurements).

Similarly, RC, BC and BW significantly correlated with gender with correlations of 0.40, 0.45 and 0.51

found, respectively. As a result, a correction took place to separate out any gender influences before

performing the Fishers exact test (Table 9). The Fishers exact test shows that RC and BC are

correlated with the body condition of the ferret. A large RC and BC classification resulted in high odds

for a ferret to be classified as obese (OR=9.5 and 16.7, respectively; Table 11). Similar findings were

observed for BW, where obese animals were more likely to be classified as average or heavy (OR=8.0

and 3.4., respectively, Table 11) Alternatively, an underweight animal was found 9.1 (RC & BW) and

11.1 (BC) times more likely to be classified as a small/light animal than a large/heavy animal.

Moreover, obese ferrets tended to be younger than ferrets assigned an optimal or too lean body

condition (p=0.09; Figure 8). However, a Fisher exact test showed that, age, just as gender, was not

significantly correlated with the body condition of the ferret (p=0.57 and p=0.84, respectively).

48

Variables Categories Overweight vs the rst Optimal vs the rest Underweight vs the rest OR 95% CI OR 95% CI OR 95% CI RC† Small 1 Ref 1 Ref 1 Ref

average 0.9 0.05-15.2 5 0.94-26.5 0.2 0.04-1.1

big 9.6 1.0-95.0 1.3 0.3-6.5 0.1 0.02-0.7 BC† Small 1 Ref 1 Ref 1 Ref

average 10.0* 0.5-215.9* 2.2 0.5-11.1 0.1 0.02-0.8 big 16.7* 0.8-331.5* 1.6 0.4-7.1 0.1 0.01-0.6

BW† light 1 Ref 1 Ref 1 Ref

average 8.0 0.7-88.2 0.7 0.1-4.0 0.4 0.1-2.1

heavy 3.4 0.3-35.0* 3.2 0.7-14.2 0.1 0.02-0.7

† corrected for gender influence on body size

Figure 8: Correlation between Age and Body condition. Obese animals tend to be younger (2.6 ± 2.0 years,

p=0.09) than animals in optimal condition (3.7 ± 1.7 years) and underweight animals (3.9 ± 1.6 years).

Table 11: Univariate analysis of the morphometric measurements with p<0.2 in the Fisher exact test. RC=ribcage

circumference, BC=belly circumference and BW=bodyweight

49

Figure 9: Relationships of VBL, DBL, LIM and BW with gender in 41 ferrets. Males have a significantly higher VBL

(41.2 ± 3.7) than females (33.9 ± 2.7; p=9.62E-09). The same is observed for DBL and LIG measurements where

male DBL and LIG are on average 43.1 ± 3.6 and 8.0 ± 0.9 and female DBL and LIG measurements are 35.7 ± 2.3

and 6.4 ± .9, respectively. The gender differences are for both DBL and LIG measurements significant (p=1.38E-

09 and 2.05E-06, respectively). Similarly, male body weights (1317.16 ± 339) are higher than female body

weights (759 ± 220; p=2E-16). M=male, F=female. Scales on the y-axis are in cm and for BW in grams.

50

The BCS-model:

For each body condition category, a multivariable logistic regression was performed, giving the 5 best

models per body condition. Of these 5 models per body condition, the best model for developing a

BCS-chart was chosen and can be seen in Table 12. The models were selected based on the number

of variables included and their p-values in the model. From these best models, models 2 and 3 each

have two variables with one or more non-significant categories, but because these models were

found to result in the best fit, these variables remained in the model. In contrast to the other

selected models, model 1 only included one significant category. However, since other models

resulted in a lower quality of fit, this model was rendered the best for the analysed data.

With these three final models a BCS-chart was developed (Figure 10). For each variable, the most

common description per body condition (based on OR values) was implemented on the chart, after

which photographs of two ferrets per body condition were added.

Table 12: The best logistic regression models for each body condition, selected based on the number of

variables included and their p-values. These models were implemented in the BCS-chart.

Model 1: obese animals vs not obese animals

Variables category p-value OR 95% CI

palpability of the ischial tuberosity

easily palpable 1 Ref

palpable with some pressure 0.13 0.67 0.67-2.11

palpability of the ribs very easily palpable 1 Ref

easily palpable 1.0 0.99 0.41-1.11

palpable with some pressure 0.37 1.23 0.65-1.51

hardly/not palpable 0.02 3.45 1.33-8.96 abdominal shape

tucked in abdomen normal abdominal shape distended abdomen

1 Ref 0.54 0.89 0.61-1.30 0.29 1.23 0.84-1.80

BW Light 1 Ref average 0.40 1.17 0.81-1.72 heavy 0.23 0.8 0.56-1.14

Model 2: animals with an optimal weight vs obese and underweight ferrets palpability of the ischial tuberosity

easily palpable 1 Ref palpable with some pressure 0.04 1.94 1.07-5.51

rib fat coverage no fat present 1 Ref scant fat/muscle coverage 0.05 1.91 1.03-3.54 little fat/muscle coverage 0.001 3.12 1.66—5.86 moderate fat/muscle coverage 0.03 2.40 1.17-4.92 substantial fat/muscle layer 0.02 3.89 1.40-10.82 unknown 0.004 3.06 1.50-6.21 palpability of the ribs very easily palpable 1 Ref easily palpable 0.03 0.44 0.22-0.90 palpable with some pressure 0.01 0.32 0.14-0.69 hardly/not palpable 0.001 0.06 0.01-0.25 abdominal shape


1 Ref 0.003 2.08 1.33-3.28 0.008 2.06 1.26-3.37

estimation of abdominal fat content <1 cm

1 Ref

51

≥ 1 cm 0.67 1.09 0.73-1.64 unknown 0.33 1.22 0.82-1.81 BW Light 1 Ref

average 0.23 0.73 0.44-1.20 Heavy 0.10 1,45 0.94-2.23

Model 3: underweight animals vs obese animals and animals with an optimal weight palpability of the cervical and thoracic vertebrae

very easily palpable 1 Ref easily palpable 0.003 0.33 0.17-0.64

palpable with some pressure 0.009 0.43 0.24-0.78 hardly/not palpable 0.007 0.39 0.21-0.74 palpability of the ischial tuberosity

easily palpable 1 Ref palpable with some pressure 0.26 0.82 0.58-1.15

abdominal shape


1 Ref 0.002 0.56 0.41-0.78 2.29E-06 0.36 0.25-0.51

estimation of abdominal fat content

<1 cm 1 Ref ≥ 1 cm 0.36 0.88 0.67-1.15 unknown 0.003 0.61 0.46-0.82

BW Light 1 Ref average 0.57 1.10 0.80-1.52 heavy 0.41 0.88 0.66-1.18

52

Figure 10: The BCS-chart for ferrets (pilot version)

53

Discussion It is important to be able to objectively evaluate the body condition of the ferret, because losing

weight in ferrets is often a primary indicator of disease. For cats, lots of different body condition

scoring methods have already been tested and validated. Therefore, in this study a pilot body

condition scoring system for the ferret was developed, based on what is already known and used in

cats.

All palpations executed in this study were significantly correlated with the body condition. However,

in correspondence with the best fit models, only palpations of the ischial tuberosity, ribs and cervical

and thoracic vertebrae, rib fat coverage, abdominal shape, estimation of abdominal fat content and

BW were included in the BCS-chart. All of these palpations have been used in previous BCS-charts for

cats (59,67,70). The pilot BCS-chart for ferrets is thus comparable with other systems. Peron et al.

(2016) determined that adding pictures to the BCS-chart enhanced the owners performance in

correctly estimating the body condition (65). Even though a specific visual inspection proved

unachievable because of the ferrets fur, a general impression of body condition can be created based

on body posture. For this reason pictures are included in the BCS-chart (appendix 8).

In order to determine the size of the ferret, VBL, DBL and LIM, as described by Jones et al. (2016) and

Hawthorne et al. (2005), were measured (71,116). VBL, DBL and LIM in ferrets were not correlated

with body condition, corresponding with cats, where LIM shows little correlation with BF% (r2<0,15),

thus rendering it an adequate parameter to be used for estimating body size in cats as well as ferrets

(71). Since the RC was found to be highly correlated with BF% in cats (71), it was assumed that

similar findings would be observed in ferrets. In addition, BC was also considered as a parameter that

would be highly dependent on the ferrets body condition, although no evidence was found in the

literature to demonstrate this relationship in ferrets or other animals. The found relation of RC and

BC with body condition confirmed these assumptions even though correlations in univariate analysis

were low. Splenomegaly is frequently seen in ferrets that are 2 years or older (118), which can

potentially interfere with the BC, making the results less reliable and correlations lower. The low

correlation between RC and BW can be explained by breathing of the animal, which makes an

objective assessment of the RC difficult.

Moreover, ferret size was found to be depended on gender. Male ferrets were significantly larger

(VBL, DBL, LIM, RC, BC and BW) than female ferrets. In this study population female ferrets weighed

between 515 and 1385g, males between 828 and 2390g. These gender differences in BW have been

included in the BCS-chart. Even though body measurements per gender are unreported, gender

difference in BW is well reported and similar values have been described (male 1-2 kg, females 0.6-1

kg; (119).

However the small sample size limits the reliability of this study. Low sample sizes per body condition

lead to zero animals being assigned to certain categories in the contingency table (Table 8 & 9),

making manual calculation of OR, necessary. It is unknown if these 0s are ‘true zeros’ or accidents of

sampling, making more than a rough estimation by manual calculation impossible. Further research

is therefore necessary to better understand the possibilities and restrictions of the pilot BCS-chart for

ferrets. The variables tested could be narrowed down by excluding variables that evaluate the same

body components or are proven not to correlate with the body condition. For example BW might not

be necessary for body condition evaluation. Also a choice could be made between estimating the

palpability of the ribs and palpating rib fat coverage. By developing standard protocols for the

evaluation of the ferrets and by using one scale for BW, the consistency of the data could be

54

improved. Also, using a small scale indicator (like a ruler) or a different, scaled, background as

described by Gant et al. (2016) for the photographs could help in interpreting and comparing the

photographs (120). The scale indicator would make it possible to use software like Coach to analyse

the photographs.

Moreover, in the study as performed, seasonal changes in BCS were not taken into account.

However, it is well known that in ferrets, large seasonal weight changes can occur. In fall, ferrets

usually gain weight and store additional fat for the upcoming winter, whereas they will often start to

lose weight in the spring again. As a result, a ferret’s BW can fluctuate as much as 40% (119), which

may subsequently have an effect on the animal’s BCS as well. It may thus be possible that optimal

body condition of a ferret differs dependent on the time of year, rendering it necessary to create

BCS-charts and reference values for the winter and summer seasons. Further research will be

necessary to determine whether this would indeed be needed. These seasonal weight influences can

also have influenced this study population. Data were collected in spring season, causing a low

number of obese ferrets (n=9) to be encountered.

In addition to a repetition of the study in the winter months to evaluate the effect of season on BCS,

validation studies are necessary. For this purpose, repeatability and reproducibility of all the

measurements and estimates need to be determined. In addition, comparative studies of BCS-results

with BF% estimated by DEXA scans will be needed to determine the accuracy of the developed BCS-

chart. Moreover these DEXA-scans could be useful to help further divide the BCS in additional classes

(i.e. minimum of 5 rather than 3), which was currently impossible due to the small number of ferrets

analysed. However, in order to be able to make this distinction, a sufficient number of ferrets with a

high range in body conditions (i.e. covering the range from cachectic to severely obese) would need

to be evaluated using both methods.

However it has to be taken into account that these evaluations of animals based on a body condition

score take practice, as is known from feline literature. Higher interrater-agreements (reproducibility)

is seen between veterinarians and trained observers than between veterinarians and untrained

observers (67,70). The researcher (I.B.) had no training or previous experience in evaluating body

conditions, which could have influenced the results of this study. Further research in which multiple

observers, experienced and inexperienced, evaluate ferrets with the same palpations is therefore

necessary to determine reproducibility.

The BCS-chart is able to distinguish ferrets in optimal body condition from obese or underweight

ferrets. It is a promising, easily applicable tool that can aid owners and veterinarians in the

estimation of body condition.

Conclusion In this study, a new body condition score system based on a BCS-chart was developed and found to

be a promising method for evaluation of a ferret’s body condition. However, further research will be

necessary in order to validate the system and determine its reliability.

55

Acknowledgements: I would like to thank dr. Yvonne van Zeeland for guiding me through the project and Hans Vernooij

MSc for helping me out with the statistics. Special thanks further goes to Rine Oddens, Stephenie

Baas, and Illonka van Lieshout of the foundation ‘Frettig gestoord’, Marianne Boymans of foundation

‘De Fret’ and Chaimel Lerou of foundation “Fret en Welzijn” for allowing me to measure the ferrets

in their shelters and their support in finding more ferrets to measure. I would also like to thank

Hanneke Roest, a ferret specialist veterinarian, for showing me how she determines the body

condition of ferrets and letting me photograph her patients. Both pet owners Doris Hoenders and

Dian de Mooij Last, have been really generous to let me measure their ferrets. Lastly, I want to thank

dr. Nico Schoemaker for lending me equipment needed for the photographs.

56

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Appendices

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Appendix 1 The 9-point BCS system The purine BCS system was developed by Laflamme et al. in 1997. It is a 9-point scale that with the

aid of pictures, helps the observer choose the right body condition for their cat.

Source: webpage “Fit or Fat: Your Pet's Body Condition Score (BCS)” (121)

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Appendix 2 The 5-point BCS system The 5-point BCS system is very much similar to the 9-point system. If the animals are scored in half-

steps, it works on the same scale as the 9-point system.

Source: Shovellar e.a. (2014) (67)

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Appendix 3 The 6-point BCS system This 6-point BCS system only relies on the body shape of the animal. No descriptions of the

palpability of skeletal components or fat estimates are given.

Emaciated Lean Optimally lean Optimal Heavy obese

Source: Doria-Rose and Scarlott (2000) (69)

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Appendix 4 S.H.A.P.E. flow chart and table S.H.A.P.E., Size Health And Physical Evaluation, does not use pictures to aid the observer. With the

help of the flow chart, the observer guided step by step through the process of evaluating the body

condition. Eventually a score from A-G is given, whereby D is considered ideal.

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Source: WALTHAM (122)

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Appendix 5: Muscle mass score chart cat The muscle mass is scored in 4 categories based on palpations of muscle over the spine, and head.

Source: Webpage: WSAVA Nutritional toolkit (123)

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Appendix 6: Phase 1 registration table This Dutch form, was used during the study to register the palpability and visibility of the different

variables quickly and in an organised way.

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Appendix 7: Phase 1 registration table, morphometric measurements The second Dutch form used in the research. Morphometric measurements and BW were registered

in this table. Names of the ferret and the shelter were written above the table.

scoring body condition in the ferret: development of a

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